Recommender Systems - Content Based Systems and Collaborative Filtering.ipynb

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    "# <font color='green'>Recommender Systems - A Comprehensive Overview</font>\n",
    "\n",
    "\n",
    "A recommender system, or a recommendation system, is a subclass of information filtering system that provides suggestions for items that are most pertinent to a user. Typically, the suggestions refer to various decision-making processes, such as what movie to watch, product to purchase, what music to listen to, or what online news to read. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer.\n",
    "\n",
    "Recommender systems are used in a variety of areas, with commonly recognised examples taking the form of playlist generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms and open web content recommenders. There are also popular recommender systems for specific topics like restaurants and online dating.\n",
    "\n",
    "Recommender systems function with two kinds of information:\n",
    "\n",
    "-  Characteristic information - This is information about items (keywords, categories, etc.) and users (preferences, profiles, etc.).\n",
    "-  User-item interactions - This is information such as ratings, number of purchases, likes, etc\n",
    "\n",
    "\n",
    "Based on this, we can distinguish between three algorithms used in recommender systems:\n",
    "\n",
    "-  Content-based systems, which use characteristic information\n",
    "-  Collaborative filtering systems, which are based on user-item interactions\n",
    "-  Hybrid systems, which combine both types of information with the aim of avoiding problems that are generated when working with just one kind\n",
    "\n",
    "In this notebook, we will be implementing Content-based and Collaborative Filtering systems to recommend movies by using Movies Metadata collected from TMDB (https://www.themoviedb.org/) which consists of 26,000,000 ratings and 750,000 tag applications applied to 46,000 movies by 270,000 users. The data used in the notebook can be found here (https://www.kaggle.com/code/rounakbanik/movie-recommender-systems/data)"
   ]
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    "## <font color='green'>Table of Contents</font>\n",
    "\n",
    "- <a href='#the_destination_1'>Section 1 - Exploratory Data Analysis</a>\n",
    "    - <a href='#the_destination_2'>1.1 Dropping Features</a>\n",
    "    - <a href='#the_destination_3'>1.2 Updating Features</a>\n",
    "    - <a href='#the_destination_4'>1.3 Feature Engineering</a>\n",
    "\n",
    "    \n",
    "- <a href='#the_destination_5'>Section 2 - Simple Recommender System</a>\n",
    "    - <a href='#the_destination_6'>2.1 Creating a simple recommender system to show the top N movies at an overall level</a>\n",
    "    - <a href='#the_destination_7'>2.2 Creating a simple recommender system to show the top N movies based on genre</a>\n",
    "\n",
    "    \n",
    "- <a href='#the_destination_8'>Section 3 - Content Based Recommender System</a>\n",
    "    - <a href='#the_destination_9'>3.1 Movie Overview Based Recommender</a>\n",
    "    - <a href='#the_destination_10'>3.2 Movie Metadata Based Recommender</a>\n",
    "\n",
    "    \n",
    "- <a href='#the_destination_11'>Section 4 - Collaborative Filtering System</a>\n",
    "    - <a href='#the_destination_12'>4.1 Memory-Based Systems</a>\n",
    "        - <a href='#the_destination_13'>4.1.1 User-Based Collaborative Filtering</a>\n",
    "        - <a href='#the_destination_14'>4.1.2 Item-Based Collaborative Filtering</a>\n",
    "    - <a href='#the_destination_15'>4.2 Model-Based Systems</a>"
   ]
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  {
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   "id": "43eeaa23",
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from ast import literal_eval\n",
    "import operator\n",
    "\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "from sentence_transformers import SentenceTransformer\n",
    "\n",
    "from surprise import Reader, Dataset\n",
    "from surprise.model_selection import cross_validate\n",
    "\n",
    "%matplotlib inline\n",
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "68e1cf17",
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       "   adult                              belongs_to_collection    budget  \\\n",
       "0  False  {'id': 10194, 'name': 'Toy Story Collection', ...  30000000   \n",
       "1  False                                                NaN  65000000   \n",
       "2  False  {'id': 119050, 'name': 'Grumpy Old Men Collect...         0   \n",
       "3  False                                                NaN  16000000   \n",
       "4  False  {'id': 96871, 'name': 'Father of the Bride Col...         0   \n",
       "\n",
       "                                              genres  \\\n",
       "0  [{'id': 16, 'name': 'Animation'}, {'id': 35, '...   \n",
       "1  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   \n",
       "2  [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...   \n",
       "3  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...   \n",
       "4                     [{'id': 35, 'name': 'Comedy'}]   \n",
       "\n",
       "                               homepage     id    imdb_id original_language  \\\n",
       "0  http://toystory.disney.com/toy-story    862  tt0114709                en   \n",
       "1                                   NaN   8844  tt0113497                en   \n",
       "2                                   NaN  15602  tt0113228                en   \n",
       "3                                   NaN  31357  tt0114885                en   \n",
       "4                                   NaN  11862  tt0113041                en   \n",
       "\n",
       "                original_title  \\\n",
       "0                    Toy Story   \n",
       "1                      Jumanji   \n",
       "2             Grumpier Old Men   \n",
       "3            Waiting to Exhale   \n",
       "4  Father of the Bride Part II   \n",
       "\n",
       "                                            overview  ... release_date  \\\n",
       "0  Led by Woody, Andy's toys live happily in his ...  ...   1995-10-30   \n",
       "1  When siblings Judy and Peter discover an encha...  ...   1995-12-15   \n",
       "2  A family wedding reignites the ancient feud be...  ...   1995-12-22   \n",
       "3  Cheated on, mistreated and stepped on, the wom...  ...   1995-12-22   \n",
       "4  Just when George Banks has recovered from his ...  ...   1995-02-10   \n",
       "\n",
       "       revenue runtime                                   spoken_languages  \\\n",
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       "\n",
       "     status                                            tagline  \\\n",
       "0  Released                                                NaN   \n",
       "1  Released          Roll the dice and unleash the excitement!   \n",
       "2  Released  Still Yelling. Still Fighting. Still Ready for...   \n",
       "3  Released  Friends are the people who let you be yourself...   \n",
       "4  Released  Just When His World Is Back To Normal... He's ...   \n",
       "\n",
       "                         title  video vote_average vote_count  \n",
       "0                    Toy Story  False          7.7     5415.0  \n",
       "1                      Jumanji  False          6.9     2413.0  \n",
       "2             Grumpier Old Men  False          6.5       92.0  \n",
       "3            Waiting to Exhale  False          6.1       34.0  \n",
       "4  Father of the Bride Part II  False          5.7      173.0  \n",
       "\n",
       "[5 rows x 24 columns]"
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   ],
   "source": [
    "md = pd.read_csv('C:/Users/balaj/Downloads/archive/movies_metadata.csv')\n",
    "md.head()"
   ]
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    "## <font color='green'> <a id='the_destination_1'>Section 1 - Exploratory Data Analysis (EDA)</a></font>\n",
    "\n",
    "Before going into the nuts and bolts of the Recommender systems, let's first take a look at the data and perform some Exploratory Data Analysis. This would help us to truly understand what the data contains and help us create a better model."
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45466 entries, 0 to 45465\n",
      "Data columns (total 24 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   adult                  45466 non-null  object \n",
      " 1   belongs_to_collection  4494 non-null   object \n",
      " 2   budget                 45466 non-null  object \n",
      " 3   genres                 45466 non-null  object \n",
      " 4   homepage               7782 non-null   object \n",
      " 5   id                     45466 non-null  object \n",
      " 6   imdb_id                45449 non-null  object \n",
      " 7   original_language      45455 non-null  object \n",
      " 8   original_title         45466 non-null  object \n",
      " 9   overview               44512 non-null  object \n",
      " 10  popularity             45461 non-null  object \n",
      " 11  poster_path            45080 non-null  object \n",
      " 12  production_companies   45463 non-null  object \n",
      " 13  production_countries   45463 non-null  object \n",
      " 14  release_date           45379 non-null  object \n",
      " 15  revenue                45460 non-null  float64\n",
      " 16  runtime                45203 non-null  float64\n",
      " 17  spoken_languages       45460 non-null  object \n",
      " 18  status                 45379 non-null  object \n",
      " 19  tagline                20412 non-null  object \n",
      " 20  title                  45460 non-null  object \n",
      " 21  video                  45460 non-null  object \n",
      " 22  vote_average           45460 non-null  float64\n",
      " 23  vote_count             45460 non-null  float64\n",
      "dtypes: float64(4), object(20)\n",
      "memory usage: 8.3+ MB\n"
     ]
    }
   ],
   "source": [
    "md.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c6a5a5f",
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   "source": [
    "We can see that the data contains 45,466 rows and 24 columns where each record contains information about a particular movie.\n",
    "Only 4 columns have numerical datatype (float to be precise) - revenue, runtime, vote_average, vote_count\n",
    "\n",
    "There are a lot of columns with missing/incomplete data. Similarly there are columns with categorical data that can be converted to numerical data to improve the performance of the model. Let's take a look at the key things we need to do next before we move onto the model.\n",
    "\n",
    "### <font color='green'>Dropping Features:</font>\n",
    "Based on the data and looking at their description, we can ignore the following columns moving forward as they don't bring any value to our analyses:\n",
    "1) adult - would have been a good feature to use if there was no class imbalance. In the dataset, only 9 records have adult as True with all of the remaining records having False\n",
    "2) homepage \n",
    "3) imdb_id\n",
    "4) poster_path\n",
    "5) video - class imbalance and hence can be removed\n",
    "\n",
    "\n",
    "### <font color='green'>Updating Features:</font>\n",
    "As mentioned earlier, there are some columns that have missing values. Usually most datasets will have misssing data, data with errors or typos. In our dataset we need to do the following:\n",
    "1) Update/Remove the records with missing values for vote_average and vote_count\n",
    "2) Update belongs_to_collection to numerical feature - 0 if null and 1 if not null\n",
    "3) Update the genres column by showing just the genre information\n",
    "4) Update the production_companies column by showing just the production companies' names\n",
    "5) Update the production_countries column by just showing the country names\n",
    "6) Update the spoken_languages column by just showing the language names\n",
    "\n",
    "The reason we're converting some categorical features into numerical feature is to improve the performance of the model as ML models run better with numerical data.\n",
    "\n",
    "### <font color='green'>Feature Engineering:</font>\n",
    "Instead of just sticking to the features we have, it's always a good idea to explore, create and test new features (created based on existing features) as there's always a good chance of them boosting your model's performance. Here we can look at creating the following feature:\n",
    "1) Creating a year column based on the release date\n",
    "2) Explore budget and revenue features to create a new feature which can either be a range of values or scale (High/Medium/Low)\n",
    "3) Filter for only those movies which have been released (status = Released)\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### <font color='green'><a id='the_destination_2'>1.1 Dropping Features</a></font>"
   ]
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       "(45466, 19)"
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     "execution_count": 4,
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   "source": [
    "md.drop(['adult', 'homepage', 'imdb_id', 'poster_path', 'video'] , axis=1, inplace=True)\n",
    "md.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "341c538c",
   "metadata": {},
   "source": [
    "### <font color='green'><a id='the_destination_3'>1.2 Updating Features</a></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8a64c561",
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       "      <th>19729</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 28, 'name': 'Action'}, {'id': 53, 'nam...</td>\n",
       "      <td>82663</td>\n",
       "      <td>en</td>\n",
       "      <td>Midnight Man</td>\n",
       "      <td>British soldiers force a recently captured IRA...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19730</th>\n",
       "      <td>0.065736</td>\n",
       "      <td>/ff9qCepilowshEtG2GYWwzt2bs4.jpg</td>\n",
       "      <td>[{'name': 'Carousel Productions', 'id': 11176}...</td>\n",
       "      <td>1997-08-20</td>\n",
       "      <td>104.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29502</th>\n",
       "      <td>{'id': 122661, 'name': 'Mardock Scramble Colle...</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 878, ...</td>\n",
       "      <td>122662</td>\n",
       "      <td>ja</td>\n",
       "      <td>マルドゥック・スクランブル 排気</td>\n",
       "      <td>Third film of the Mardock Scramble series.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29503</th>\n",
       "      <td>1.931659</td>\n",
       "      <td>/zV8bHuSL6WXoD6FWogP9j4x80bL.jpg</td>\n",
       "      <td>[{'name': 'Aniplex', 'id': 2883}, {'name': 'Go...</td>\n",
       "      <td>2012-09-29</td>\n",
       "      <td>68.0</td>\n",
       "      <td>[{'iso_639_1': 'ja', 'name': '日本語'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35586</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 10770, 'name': 'TV Movie'}, {'id': 28,...</td>\n",
       "      <td>249260</td>\n",
       "      <td>en</td>\n",
       "      <td>Avalanche Sharks</td>\n",
       "      <td>A group of skiers are terrorized during spring...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35587</th>\n",
       "      <td>2.185485</td>\n",
       "      <td>/zaSf5OG7V8X8gqFvly88zDdRm46.jpg</td>\n",
       "      <td>[{'name': 'Odyssey Media', 'id': 17161}, {'nam...</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>82.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Beware Of Frost Bites</td>\n",
       "      <td>False</td>\n",
       "      <td>4.3</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   belongs_to_collection  \\\n",
       "19729                                                NaN   \n",
       "19730                                           0.065736   \n",
       "29502  {'id': 122661, 'name': 'Mardock Scramble Colle...   \n",
       "29503                                           1.931659   \n",
       "35586                                                NaN   \n",
       "35587                                           2.185485   \n",
       "\n",
       "                                 budget  \\\n",
       "19729                                 0   \n",
       "19730  /ff9qCepilowshEtG2GYWwzt2bs4.jpg   \n",
       "29502                                 0   \n",
       "29503  /zV8bHuSL6WXoD6FWogP9j4x80bL.jpg   \n",
       "35586                                 0   \n",
       "35587  /zaSf5OG7V8X8gqFvly88zDdRm46.jpg   \n",
       "\n",
       "                                                  genres          id  \\\n",
       "19729  [{'id': 28, 'name': 'Action'}, {'id': 53, 'nam...       82663   \n",
       "19730  [{'name': 'Carousel Productions', 'id': 11176}...  1997-08-20   \n",
       "29502  [{'id': 16, 'name': 'Animation'}, {'id': 878, ...      122662   \n",
       "29503  [{'name': 'Aniplex', 'id': 2883}, {'name': 'Go...  2012-09-29   \n",
       "35586  [{'id': 10770, 'name': 'TV Movie'}, {'id': 28,...      249260   \n",
       "35587  [{'name': 'Odyssey Media', 'id': 17161}, {'nam...  2014-01-01   \n",
       "\n",
       "      original_language                            original_title  \\\n",
       "19729                en                              Midnight Man   \n",
       "19730             104.0  [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "29502                ja                          マルドゥック・スクランブル 排気   \n",
       "29503              68.0      [{'iso_639_1': 'ja', 'name': '日本語'}]   \n",
       "35586                en                          Avalanche Sharks   \n",
       "35587              82.0  [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "\n",
       "                                                overview  \\\n",
       "19729  British soldiers force a recently captured IRA...   \n",
       "19730                                           Released   \n",
       "29502         Third film of the Mardock Scramble series.   \n",
       "29503                                           Released   \n",
       "35586  A group of skiers are terrorized during spring...   \n",
       "35587                                           Released   \n",
       "\n",
       "                  popularity production_companies production_countries  \\\n",
       "19729                    NaN                  NaN                  NaN   \n",
       "19730                    NaN                False                  6.0   \n",
       "29502                    NaN                  NaN                  NaN   \n",
       "29503                    NaN                False                  7.0   \n",
       "35586                    NaN                  NaN                  NaN   \n",
       "35587  Beware Of Frost Bites                False                  4.3   \n",
       "\n",
       "      release_date  revenue  runtime spoken_languages status tagline title  \\\n",
       "19729          NaN      NaN      NaN              NaN    NaN     NaN   NaN   \n",
       "19730            1      NaN      NaN              NaN    NaN     NaN   NaN   \n",
       "29502          NaN      NaN      NaN              NaN    NaN     NaN   NaN   \n",
       "29503           12      NaN      NaN              NaN    NaN     NaN   NaN   \n",
       "35586          NaN      NaN      NaN              NaN    NaN     NaN   NaN   \n",
       "35587           22      NaN      NaN              NaN    NaN     NaN   NaN   \n",
       "\n",
       "       vote_average  vote_count  \n",
       "19729           NaN         NaN  \n",
       "19730           NaN         NaN  \n",
       "29502           NaN         NaN  \n",
       "29503           NaN         NaN  \n",
       "35586           NaN         NaN  \n",
       "35587           NaN         NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md[md['vote_average'].isna() == True]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "703c994d",
   "metadata": {},
   "source": [
    "As we can see, out of the 6 records with null values for vote_average (& vote_count), 3 of them have no information for most of the columns while 3 records have information wrongly encoded. Hence it's better to drop these columns instead of updating the features since it's only 6 records out of 45466 (0.013%) and hence there should be no impact to our analyses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8a9b6920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45460, 19)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md.drop(md[md['vote_average'].isna() == True].index, axis=0, inplace=True)\n",
    "md.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "802bbe3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Updating the belongs_to_collection column to a numerical feature - 0 if null and 1 if not null\n",
    "\n",
    "md['belongs_to_collection'] = md['belongs_to_collection'].notna().astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0bbab172",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Updating the genres column by extracting the genres and storing it in a list\n",
    "\n",
    "md['genres'] = md['genres'].apply(literal_eval).apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "513c1d76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Updating the production companies column by extracting the company names and storing it in a list\n",
    "\n",
    "md['production_companies'] = md['production_companies'].apply(literal_eval).apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f9e4e80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Updating the production countries column by extracting the country names and storing it in a list\n",
    "\n",
    "md['production_countries'] = md['production_countries'].apply(literal_eval).apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "26e2a725",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Updating the spoken languages column by extracting the languages and storing it in a list\n",
    "\n",
    "md['spoken_languages'] = md['spoken_languages'].apply(literal_eval).apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "871d5be1",
   "metadata": {},
   "source": [
    "### <font color='green'><a id='the_destination_4'>1.3 Feature Engineering</a></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e9696de5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new column for year\n",
    "\n",
    "md['year'] = pd.to_datetime(md['release_date'], errors='coerce').dt.year.convert_dtypes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "92b34ffd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45014, 19)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering the dataset to keep only Released Movies and then dropping the status column\n",
    "\n",
    "md = md[md['status'] == 'Released']\n",
    "del md['status']\n",
    "md.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a6678303",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>belongs_to_collection</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>production_countries</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>30000000</td>\n",
       "      <td>[Animation, Comedy, Family]</td>\n",
       "      <td>862</td>\n",
       "      <td>en</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Led by Woody, Andy's toys live happily in his ...</td>\n",
       "      <td>21.946943</td>\n",
       "      <td>[Pixar Animation Studios]</td>\n",
       "      <td>[United States of America]</td>\n",
       "      <td>1995-10-30</td>\n",
       "      <td>373554033.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>[English]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5415.0</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>65000000</td>\n",
       "      <td>[Adventure, Fantasy, Family]</td>\n",
       "      <td>8844</td>\n",
       "      <td>en</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>When siblings Judy and Peter discover an encha...</td>\n",
       "      <td>17.015539</td>\n",
       "      <td>[TriStar Pictures, Teitler Film, Interscope Co...</td>\n",
       "      <td>[United States of America]</td>\n",
       "      <td>1995-12-15</td>\n",
       "      <td>262797249.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>[English, Français]</td>\n",
       "      <td>Roll the dice and unleash the excitement!</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2413.0</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[Romance, Comedy]</td>\n",
       "      <td>15602</td>\n",
       "      <td>en</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>A family wedding reignites the ancient feud be...</td>\n",
       "      <td>11.7129</td>\n",
       "      <td>[Warner Bros., Lancaster Gate]</td>\n",
       "      <td>[United States of America]</td>\n",
       "      <td>1995-12-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>[English]</td>\n",
       "      <td>Still Yelling. Still Fighting. Still Ready for...</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>6.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>16000000</td>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>31357</td>\n",
       "      <td>en</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
       "      <td>3.859495</td>\n",
       "      <td>[Twentieth Century Fox Film Corporation]</td>\n",
       "      <td>[United States of America]</td>\n",
       "      <td>1995-12-22</td>\n",
       "      <td>81452156.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>[English]</td>\n",
       "      <td>Friends are the people who let you be yourself...</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>6.1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[Comedy]</td>\n",
       "      <td>11862</td>\n",
       "      <td>en</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>Just when George Banks has recovered from his ...</td>\n",
       "      <td>8.387519</td>\n",
       "      <td>[Sandollar Productions, Touchstone Pictures]</td>\n",
       "      <td>[United States of America]</td>\n",
       "      <td>1995-02-10</td>\n",
       "      <td>76578911.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>[English]</td>\n",
       "      <td>Just When His World Is Back To Normal... He's ...</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>5.7</td>\n",
       "      <td>173.0</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   belongs_to_collection    budget                        genres     id  \\\n",
       "0                      1  30000000   [Animation, Comedy, Family]    862   \n",
       "1                      0  65000000  [Adventure, Fantasy, Family]   8844   \n",
       "2                      1         0             [Romance, Comedy]  15602   \n",
       "3                      0  16000000      [Comedy, Drama, Romance]  31357   \n",
       "4                      1         0                      [Comedy]  11862   \n",
       "\n",
       "  original_language               original_title  \\\n",
       "0                en                    Toy Story   \n",
       "1                en                      Jumanji   \n",
       "2                en             Grumpier Old Men   \n",
       "3                en            Waiting to Exhale   \n",
       "4                en  Father of the Bride Part II   \n",
       "\n",
       "                                            overview popularity  \\\n",
       "0  Led by Woody, Andy's toys live happily in his ...  21.946943   \n",
       "1  When siblings Judy and Peter discover an encha...  17.015539   \n",
       "2  A family wedding reignites the ancient feud be...    11.7129   \n",
       "3  Cheated on, mistreated and stepped on, the wom...   3.859495   \n",
       "4  Just when George Banks has recovered from his ...   8.387519   \n",
       "\n",
       "                                production_companies  \\\n",
       "0                          [Pixar Animation Studios]   \n",
       "1  [TriStar Pictures, Teitler Film, Interscope Co...   \n",
       "2                     [Warner Bros., Lancaster Gate]   \n",
       "3           [Twentieth Century Fox Film Corporation]   \n",
       "4       [Sandollar Productions, Touchstone Pictures]   \n",
       "\n",
       "         production_countries release_date      revenue  runtime  \\\n",
       "0  [United States of America]   1995-10-30  373554033.0     81.0   \n",
       "1  [United States of America]   1995-12-15  262797249.0    104.0   \n",
       "2  [United States of America]   1995-12-22          0.0    101.0   \n",
       "3  [United States of America]   1995-12-22   81452156.0    127.0   \n",
       "4  [United States of America]   1995-02-10   76578911.0    106.0   \n",
       "\n",
       "      spoken_languages                                            tagline  \\\n",
       "0            [English]                                                NaN   \n",
       "1  [English, Français]          Roll the dice and unleash the excitement!   \n",
       "2            [English]  Still Yelling. Still Fighting. Still Ready for...   \n",
       "3            [English]  Friends are the people who let you be yourself...   \n",
       "4            [English]  Just When His World Is Back To Normal... He's ...   \n",
       "\n",
       "                         title  vote_average  vote_count  year  \n",
       "0                    Toy Story           7.7      5415.0  1995  \n",
       "1                      Jumanji           6.9      2413.0  1995  \n",
       "2             Grumpier Old Men           6.5        92.0  1995  \n",
       "3            Waiting to Exhale           6.1        34.0  1995  \n",
       "4  Father of the Bride Part II           5.7       173.0  1995  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4b223f6",
   "metadata": {},
   "source": [
    "## <font color='green'><a id='the_destination_5'>Section 2 - Simple Recommender System</a></font>\n",
    "\n",
    "We can see that the data now looks a lot cleaner and is so much easier to understand. Now that we are done with the data exploration and  feature finalization, we can proceed with the creation of the Recommender Systems. \n",
    "\n",
    "Before we get into the weeds of the major recommender systems, I would like to give a brief intro to a basic recommender system that doesn't require any models - Simple Recommender System. This system offers generalized recommendations to every user based on overall movie popularity without giving anything personalized. For example, Netflix's Top 30 is a recommendation which just shows the top 30 movies that are currently watched by people in the area or country and it depends only on the view counts. \n",
    "\n",
    "The implementation of this model is fairly simple. In our case, since we have the data from TMDB, all we have to do is sort our movies based on ratings and display the top N movies. To make it more specialized, we can add an additional argument for genre and display top N movies based on the genre.\n",
    "\n",
    "In our data, we can make use of the vote_average column to filter for the top N movies. But there's a catch. Looking at the data, we can see that there are movies with high vote_average but low vote_count. It wouldn't make sense to include movies with a rating of 10, which have been voted by just 1 person. So in order to avoid such scenarios, we can make use of the standard approach which is being used by IMDB called the Weighted Rating"
   ]
  },
  {
   "attachments": {
    "weight-avg.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "0e0fa46c",
   "metadata": {},
   "source": [
    "![weight-avg.png](attachment:weight-avg.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe924330",
   "metadata": {},
   "source": [
    "where,\n",
    "\n",
    "v - Number of people who have rated the movie (vote_count)\\\n",
    "m - Minimum number of ratings required to be listed in the chart\\\n",
    "R - Average rating of the movie (vote_average)\\\n",
    "C - Mean of ratings across the whole dataset\n",
    "\n",
    "We already have the values for v and R. Now, we need to calculate m and C in order to get the top N movies. For calculating m, we can use the 95th percentile as our cutoff. In other words, for a movie to feature in the top N list, it needs to have more votes than at least 95% of the movies in the list."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7432536e",
   "metadata": {},
   "source": [
    "### <font color='green'><a id='the_destination_6'>2.1 Creating a simple recommender system to show the top N movies at an overall level</a></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "95e5ca67",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting values m and C\n",
    "\n",
    "m = round(md['vote_count'].quantile(0.95))\n",
    "C = round(md['vote_average'].mean(), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7c7aa654",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "438\n",
      "5.6\n"
     ]
    }
   ],
   "source": [
    "print(m)\n",
    "print(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c1cbbe0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2255, 7)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering the dataset to only select those movies having atleast 438 as vote_count and selecting only the relevant features for this system\n",
    "\n",
    "simple_recommender_1 = md[(md['vote_count'] >= m)][['title', 'year', 'vote_count', 'vote_average', 'popularity', 'genres', 'overview']]\n",
    "simple_recommender_1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc227ce9",
   "metadata": {},
   "source": [
    "We end up with 2255 movies, which would be used to create the top N movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e1d55908",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a function to find the weighted rating based on the IMDB formula\n",
    "\n",
    "def weighted_rating(x):\n",
    "    v = x['vote_count']\n",
    "    R = x['vote_average']\n",
    "    return round((v*R/(v+m)) + (m*C/(m+v)),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a7b40f5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_recommender_1['weighted_rating'] = simple_recommender_1.apply(weighted_rating, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bb3af63e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>popularity</th>\n",
       "      <th>genres</th>\n",
       "      <th>overview</th>\n",
       "      <th>weighted_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>5415.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>21.946943</td>\n",
       "      <td>[Animation, Comedy, Family]</td>\n",
       "      <td>Led by Woody, Andy's toys live happily in his ...</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jumanji</td>\n",
       "      <td>1995</td>\n",
       "      <td>2413.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>17.015539</td>\n",
       "      <td>[Adventure, Fantasy, Family]</td>\n",
       "      <td>When siblings Judy and Peter discover an encha...</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Heat</td>\n",
       "      <td>1995</td>\n",
       "      <td>1886.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>17.924927</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>Obsessive master thief, Neil McCauley leads a ...</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GoldenEye</td>\n",
       "      <td>1995</td>\n",
       "      <td>1194.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>14.686036</td>\n",
       "      <td>[Adventure, Action, Thriller]</td>\n",
       "      <td>James Bond must unmask the mysterious head of ...</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Casino</td>\n",
       "      <td>1995</td>\n",
       "      <td>1343.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>10.137389</td>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>The life of the gambling paradise – Las Vegas ...</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        title  year  vote_count  vote_average popularity  \\\n",
       "0   Toy Story  1995      5415.0           7.7  21.946943   \n",
       "1     Jumanji  1995      2413.0           6.9  17.015539   \n",
       "5        Heat  1995      1886.0           7.7  17.924927   \n",
       "9   GoldenEye  1995      1194.0           6.6  14.686036   \n",
       "15     Casino  1995      1343.0           7.8  10.137389   \n",
       "\n",
       "                              genres  \\\n",
       "0        [Animation, Comedy, Family]   \n",
       "1       [Adventure, Fantasy, Family]   \n",
       "5   [Action, Crime, Drama, Thriller]   \n",
       "9      [Adventure, Action, Thriller]   \n",
       "15                    [Drama, Crime]   \n",
       "\n",
       "                                             overview  weighted_rating  \n",
       "0   Led by Woody, Andy's toys live happily in his ...              7.5  \n",
       "1   When siblings Judy and Peter discover an encha...              6.7  \n",
       "5   Obsessive master thief, Neil McCauley leads a ...              7.3  \n",
       "9   James Bond must unmask the mysterious head of ...              6.3  \n",
       "15  The life of the gambling paradise – Las Vegas ...              7.3  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_recommender_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c3f3902a",
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_recommender_1.sort_values(['weighted_rating', 'year'], ascending= [False, False], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b79589ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>popularity</th>\n",
       "      <th>genres</th>\n",
       "      <th>overview</th>\n",
       "      <th>weighted_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>8358.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>51.645403</td>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>Framed in the 1940s for the double murder of h...</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>6024.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>41.109264</td>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>Spanning the years 1945 to 1955, a chronicle o...</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12481</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>12269.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>123.167259</td>\n",
       "      <td>[Drama, Action, Crime, Thriller]</td>\n",
       "      <td>Batman raises the stakes in his war on crime. ...</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2843</th>\n",
       "      <td>Fight Club</td>\n",
       "      <td>1999</td>\n",
       "      <td>9678.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>63.869599</td>\n",
       "      <td>[Drama]</td>\n",
       "      <td>A ticking-time-bomb insomniac and a slippery s...</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>1994</td>\n",
       "      <td>8670.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>140.950236</td>\n",
       "      <td>[Thriller, Crime]</td>\n",
       "      <td>A burger-loving hit man, his philosophical par...</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23673</th>\n",
       "      <td>Whiplash</td>\n",
       "      <td>2014</td>\n",
       "      <td>4376.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>64.29999</td>\n",
       "      <td>[Drama]</td>\n",
       "      <td>Under the direction of a ruthless instructor, ...</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>1994</td>\n",
       "      <td>8147.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>48.307194</td>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>A man with a low IQ has accomplished great thi...</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>1993</td>\n",
       "      <td>4436.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>41.725123</td>\n",
       "      <td>[Drama, History, War]</td>\n",
       "      <td>The true story of how businessman Oskar Schind...</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22879</th>\n",
       "      <td>Interstellar</td>\n",
       "      <td>2014</td>\n",
       "      <td>11187.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>32.213481</td>\n",
       "      <td>[Adventure, Drama, Science Fiction]</td>\n",
       "      <td>Interstellar chronicles the adventures of a gr...</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18465</th>\n",
       "      <td>The Intouchables</td>\n",
       "      <td>2011</td>\n",
       "      <td>5410.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>16.086919</td>\n",
       "      <td>[Drama, Comedy]</td>\n",
       "      <td>A true story of two men who should never have ...</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15480</th>\n",
       "      <td>Inception</td>\n",
       "      <td>2010</td>\n",
       "      <td>14075.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>29.108149</td>\n",
       "      <td>[Action, Thriller, Science Fiction, Mystery, A...</td>\n",
       "      <td>Cobb, a skilled thief who commits corporate es...</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7000</th>\n",
       "      <td>The Lord of the Rings: The Return of the King</td>\n",
       "      <td>2003</td>\n",
       "      <td>8226.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>29.324358</td>\n",
       "      <td>[Adventure, Fantasy, Action]</td>\n",
       "      <td>Aragorn is revealed as the heir to the ancient...</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5481</th>\n",
       "      <td>Spirited Away</td>\n",
       "      <td>2001</td>\n",
       "      <td>3968.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>41.048867</td>\n",
       "      <td>[Fantasy, Adventure, Animation, Family]</td>\n",
       "      <td>A ten year old girl who wanders away from her ...</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3030</th>\n",
       "      <td>The Green Mile</td>\n",
       "      <td>1999</td>\n",
       "      <td>4166.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>19.96678</td>\n",
       "      <td>[Fantasy, Drama, Crime]</td>\n",
       "      <td>A supernatural tale set on death row in a Sout...</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2211</th>\n",
       "      <td>Life Is Beautiful</td>\n",
       "      <td>1997</td>\n",
       "      <td>3643.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>39.39497</td>\n",
       "      <td>[Comedy, Drama]</td>\n",
       "      <td>A touching story of an Italian book seller of ...</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  year  vote_count  \\\n",
       "314                         The Shawshank Redemption  1994      8358.0   \n",
       "834                                    The Godfather  1972      6024.0   \n",
       "12481                                The Dark Knight  2008     12269.0   \n",
       "2843                                      Fight Club  1999      9678.0   \n",
       "292                                     Pulp Fiction  1994      8670.0   \n",
       "23673                                       Whiplash  2014      4376.0   \n",
       "351                                     Forrest Gump  1994      8147.0   \n",
       "522                                 Schindler's List  1993      4436.0   \n",
       "22879                                   Interstellar  2014     11187.0   \n",
       "18465                               The Intouchables  2011      5410.0   \n",
       "15480                                      Inception  2010     14075.0   \n",
       "7000   The Lord of the Rings: The Return of the King  2003      8226.0   \n",
       "5481                                   Spirited Away  2001      3968.0   \n",
       "3030                                  The Green Mile  1999      4166.0   \n",
       "2211                               Life Is Beautiful  1997      3643.0   \n",
       "\n",
       "       vote_average  popularity  \\\n",
       "314             8.5   51.645403   \n",
       "834             8.5   41.109264   \n",
       "12481           8.3  123.167259   \n",
       "2843            8.3   63.869599   \n",
       "292             8.3  140.950236   \n",
       "23673           8.3    64.29999   \n",
       "351             8.2   48.307194   \n",
       "522             8.3   41.725123   \n",
       "22879           8.1   32.213481   \n",
       "18465           8.2   16.086919   \n",
       "15480           8.1   29.108149   \n",
       "7000            8.1   29.324358   \n",
       "5481            8.3   41.048867   \n",
       "3030            8.2    19.96678   \n",
       "2211            8.3    39.39497   \n",
       "\n",
       "                                                  genres  \\\n",
       "314                                       [Drama, Crime]   \n",
       "834                                       [Drama, Crime]   \n",
       "12481                   [Drama, Action, Crime, Thriller]   \n",
       "2843                                             [Drama]   \n",
       "292                                    [Thriller, Crime]   \n",
       "23673                                            [Drama]   \n",
       "351                             [Comedy, Drama, Romance]   \n",
       "522                                [Drama, History, War]   \n",
       "22879                [Adventure, Drama, Science Fiction]   \n",
       "18465                                    [Drama, Comedy]   \n",
       "15480  [Action, Thriller, Science Fiction, Mystery, A...   \n",
       "7000                        [Adventure, Fantasy, Action]   \n",
       "5481             [Fantasy, Adventure, Animation, Family]   \n",
       "3030                             [Fantasy, Drama, Crime]   \n",
       "2211                                     [Comedy, Drama]   \n",
       "\n",
       "                                                overview  weighted_rating  \n",
       "314    Framed in the 1940s for the double murder of h...              8.4  \n",
       "834    Spanning the years 1945 to 1955, a chronicle o...              8.3  \n",
       "12481  Batman raises the stakes in his war on crime. ...              8.2  \n",
       "2843   A ticking-time-bomb insomniac and a slippery s...              8.2  \n",
       "292    A burger-loving hit man, his philosophical par...              8.2  \n",
       "23673  Under the direction of a ruthless instructor, ...              8.1  \n",
       "351    A man with a low IQ has accomplished great thi...              8.1  \n",
       "522    The true story of how businessman Oskar Schind...              8.1  \n",
       "22879  Interstellar chronicles the adventures of a gr...              8.0  \n",
       "18465  A true story of two men who should never have ...              8.0  \n",
       "15480  Cobb, a skilled thief who commits corporate es...              8.0  \n",
       "7000   Aragorn is revealed as the heir to the ancient...              8.0  \n",
       "5481   A ten year old girl who wanders away from her ...              8.0  \n",
       "3030   A supernatural tale set on death row in a Sout...              8.0  \n",
       "2211   A touching story of an Italian book seller of ...              8.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Showing the top 15 movies as recommendations\n",
    "\n",
    "simple_recommender_1.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "053a8726",
   "metadata": {},
   "source": [
    "We see that 'The Shawshank Redemption', 'The Godfather' and 'The Dark Knight' are the top 3 movies based on our dataset. Not a bad list of movies to suggest for someone who's new to the system!\n",
    "\n",
    "Let us now create a function that can take in the genre, the percentile and the N count and return the top N movies in that genre accordingly. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b41a4f",
   "metadata": {},
   "source": [
    "### <font color='green'><a id='the_destination_7'>2.2 Creating a simple recommender system to show the top N movies based on genre</a></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "da91321e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple_recommender(N, genre = '', percentile = 0.95):\n",
    "    df = md.copy()\n",
    "    if genre:\n",
    "        df['genre_flag'] = df['genres'].apply(lambda x: 1 if x.count(genre) > 0 else 0)        \n",
    "        df = df[df['genre_flag'] == 1]\n",
    "    \n",
    "    m = round(df['vote_count'].quantile(percentile))\n",
    "    C = round(df['vote_average'].mean(), 1)\n",
    "    \n",
    "    simple_recommender = df[(df['vote_count'] >= m)][['title', 'year', 'vote_count', 'vote_average', 'popularity', 'genres']]\n",
    "    \n",
    "    def weighted_rating(x):\n",
    "        v = x['vote_count']\n",
    "        R = x['vote_average']\n",
    "        return round((v*R/(v+m)) + (m*C/(m+v)),1)\n",
    "    \n",
    "    simple_recommender['weighted_rating'] = simple_recommender_1.apply(weighted_rating, axis=1)\n",
    "    simple_recommender.sort_values(['weighted_rating', 'year'], ascending= [False, False], inplace = True)\n",
    "    \n",
    "    return simple_recommender.head(N)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f32b42be",
   "metadata": {},
   "source": [
    "If we keep the genre as null and get the top 15 movies, we get the same result we got earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c5cdab10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>popularity</th>\n",
       "      <th>genres</th>\n",
       "      <th>weighted_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>8358.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>51.645403</td>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>6024.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>41.109264</td>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12481</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>12269.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>123.167259</td>\n",
       "      <td>[Drama, Action, Crime, Thriller]</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2843</th>\n",
       "      <td>Fight Club</td>\n",
       "      <td>1999</td>\n",
       "      <td>9678.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>63.869599</td>\n",
       "      <td>[Drama]</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>1994</td>\n",
       "      <td>8670.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>140.950236</td>\n",
       "      <td>[Thriller, Crime]</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23673</th>\n",
       "      <td>Whiplash</td>\n",
       "      <td>2014</td>\n",
       "      <td>4376.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>64.29999</td>\n",
       "      <td>[Drama]</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>1994</td>\n",
       "      <td>8147.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>48.307194</td>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>1993</td>\n",
       "      <td>4436.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>41.725123</td>\n",
       "      <td>[Drama, History, War]</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22879</th>\n",
       "      <td>Interstellar</td>\n",
       "      <td>2014</td>\n",
       "      <td>11187.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>32.213481</td>\n",
       "      <td>[Adventure, Drama, Science Fiction]</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18465</th>\n",
       "      <td>The Intouchables</td>\n",
       "      <td>2011</td>\n",
       "      <td>5410.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>16.086919</td>\n",
       "      <td>[Drama, Comedy]</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15480</th>\n",
       "      <td>Inception</td>\n",
       "      <td>2010</td>\n",
       "      <td>14075.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>29.108149</td>\n",
       "      <td>[Action, Thriller, Science Fiction, Mystery, A...</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7000</th>\n",
       "      <td>The Lord of the Rings: The Return of the King</td>\n",
       "      <td>2003</td>\n",
       "      <td>8226.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>29.324358</td>\n",
       "      <td>[Adventure, Fantasy, Action]</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5481</th>\n",
       "      <td>Spirited Away</td>\n",
       "      <td>2001</td>\n",
       "      <td>3968.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>41.048867</td>\n",
       "      <td>[Fantasy, Adventure, Animation, Family]</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3030</th>\n",
       "      <td>The Green Mile</td>\n",
       "      <td>1999</td>\n",
       "      <td>4166.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>19.96678</td>\n",
       "      <td>[Fantasy, Drama, Crime]</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2211</th>\n",
       "      <td>Life Is Beautiful</td>\n",
       "      <td>1997</td>\n",
       "      <td>3643.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>39.39497</td>\n",
       "      <td>[Comedy, Drama]</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  year  vote_count  \\\n",
       "314                         The Shawshank Redemption  1994      8358.0   \n",
       "834                                    The Godfather  1972      6024.0   \n",
       "12481                                The Dark Knight  2008     12269.0   \n",
       "2843                                      Fight Club  1999      9678.0   \n",
       "292                                     Pulp Fiction  1994      8670.0   \n",
       "23673                                       Whiplash  2014      4376.0   \n",
       "351                                     Forrest Gump  1994      8147.0   \n",
       "522                                 Schindler's List  1993      4436.0   \n",
       "22879                                   Interstellar  2014     11187.0   \n",
       "18465                               The Intouchables  2011      5410.0   \n",
       "15480                                      Inception  2010     14075.0   \n",
       "7000   The Lord of the Rings: The Return of the King  2003      8226.0   \n",
       "5481                                   Spirited Away  2001      3968.0   \n",
       "3030                                  The Green Mile  1999      4166.0   \n",
       "2211                               Life Is Beautiful  1997      3643.0   \n",
       "\n",
       "       vote_average  popularity  \\\n",
       "314             8.5   51.645403   \n",
       "834             8.5   41.109264   \n",
       "12481           8.3  123.167259   \n",
       "2843            8.3   63.869599   \n",
       "292             8.3  140.950236   \n",
       "23673           8.3    64.29999   \n",
       "351             8.2   48.307194   \n",
       "522             8.3   41.725123   \n",
       "22879           8.1   32.213481   \n",
       "18465           8.2   16.086919   \n",
       "15480           8.1   29.108149   \n",
       "7000            8.1   29.324358   \n",
       "5481            8.3   41.048867   \n",
       "3030            8.2    19.96678   \n",
       "2211            8.3    39.39497   \n",
       "\n",
       "                                                  genres  weighted_rating  \n",
       "314                                       [Drama, Crime]              8.4  \n",
       "834                                       [Drama, Crime]              8.3  \n",
       "12481                   [Drama, Action, Crime, Thriller]              8.2  \n",
       "2843                                             [Drama]              8.2  \n",
       "292                                    [Thriller, Crime]              8.2  \n",
       "23673                                            [Drama]              8.1  \n",
       "351                             [Comedy, Drama, Romance]              8.1  \n",
       "522                                [Drama, History, War]              8.1  \n",
       "22879                [Adventure, Drama, Science Fiction]              8.0  \n",
       "18465                                    [Drama, Comedy]              8.0  \n",
       "15480  [Action, Thriller, Science Fiction, Mystery, A...              8.0  \n",
       "7000                        [Adventure, Fantasy, Action]              8.0  \n",
       "5481             [Fantasy, Adventure, Animation, Family]              8.0  \n",
       "3030                             [Fantasy, Drama, Crime]              8.0  \n",
       "2211                                     [Comedy, Drama]              8.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_recommender(N = 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d2eac64",
   "metadata": {},
   "source": [
    "Let's check for the 'Horror' genre to see what movies we get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9e7f8df5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>popularity</th>\n",
       "      <th>genres</th>\n",
       "      <th>weighted_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1213</th>\n",
       "      <td>The Shining</td>\n",
       "      <td>1980</td>\n",
       "      <td>3890.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>19.611589</td>\n",
       "      <td>[Horror, Thriller]</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1176</th>\n",
       "      <td>Psycho</td>\n",
       "      <td>1960</td>\n",
       "      <td>2405.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>36.826309</td>\n",
       "      <td>[Drama, Horror, Thriller]</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1171</th>\n",
       "      <td>Alien</td>\n",
       "      <td>1979</td>\n",
       "      <td>4564.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>23.37742</td>\n",
       "      <td>[Horror, Action, Thriller, Science Fiction]</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1158</th>\n",
       "      <td>Aliens</td>\n",
       "      <td>1986</td>\n",
       "      <td>3282.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>21.761179</td>\n",
       "      <td>[Horror, Action, Thriller, Science Fiction]</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2175</th>\n",
       "      <td>The Thing</td>\n",
       "      <td>1982</td>\n",
       "      <td>1629.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>16.83125</td>\n",
       "      <td>[Horror, Mystery, Science Fiction]</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>Jaws</td>\n",
       "      <td>1975</td>\n",
       "      <td>2628.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>19.726114</td>\n",
       "      <td>[Horror, Thriller, Adventure]</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21276</th>\n",
       "      <td>The Conjuring</td>\n",
       "      <td>2013</td>\n",
       "      <td>3169.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>14.90169</td>\n",
       "      <td>[Horror, Thriller]</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8147</th>\n",
       "      <td>Shaun of the Dead</td>\n",
       "      <td>2004</td>\n",
       "      <td>2479.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>14.902948</td>\n",
       "      <td>[Horror, Comedy]</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>The Exorcist</td>\n",
       "      <td>1973</td>\n",
       "      <td>2046.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>12.137595</td>\n",
       "      <td>[Drama, Horror, Thriller]</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14236</th>\n",
       "      <td>Zombieland</td>\n",
       "      <td>2009</td>\n",
       "      <td>3655.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>11.063029</td>\n",
       "      <td>[Comedy, Horror]</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42169</th>\n",
       "      <td>Get Out</td>\n",
       "      <td>2017</td>\n",
       "      <td>2978.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>36.894806</td>\n",
       "      <td>[Mystery, Thriller, Horror]</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39821</th>\n",
       "      <td>Train to Busan</td>\n",
       "      <td>2016</td>\n",
       "      <td>984.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>22.685169</td>\n",
       "      <td>[Action, Horror, Thriller]</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8230</th>\n",
       "      <td>Saw</td>\n",
       "      <td>2004</td>\n",
       "      <td>2255.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>23.508433</td>\n",
       "      <td>[Horror, Mystery, Crime]</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4591</th>\n",
       "      <td>The Others</td>\n",
       "      <td>2001</td>\n",
       "      <td>1708.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>11.046007</td>\n",
       "      <td>[Horror, Drama, Mystery, Thriller]</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41492</th>\n",
       "      <td>Split</td>\n",
       "      <td>2016</td>\n",
       "      <td>4461.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28.920839</td>\n",
       "      <td>[Horror, Thriller]</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   title  year  vote_count  vote_average popularity  \\\n",
       "1213         The Shining  1980      3890.0           8.1  19.611589   \n",
       "1176              Psycho  1960      2405.0           8.3  36.826309   \n",
       "1171               Alien  1979      4564.0           7.9   23.37742   \n",
       "1158              Aliens  1986      3282.0           7.7  21.761179   \n",
       "2175           The Thing  1982      1629.0           7.8   16.83125   \n",
       "1338                Jaws  1975      2628.0           7.5  19.726114   \n",
       "21276      The Conjuring  2013      3169.0           7.4   14.90169   \n",
       "8147   Shaun of the Dead  2004      2479.0           7.5  14.902948   \n",
       "1888        The Exorcist  1973      2046.0           7.5  12.137595   \n",
       "14236         Zombieland  2009      3655.0           7.2  11.063029   \n",
       "42169            Get Out  2017      2978.0           7.2  36.894806   \n",
       "39821     Train to Busan  2016       984.0           7.7  22.685169   \n",
       "8230                 Saw  2004      2255.0           7.2  23.508433   \n",
       "4591          The Others  2001      1708.0           7.4  11.046007   \n",
       "41492              Split  2016      4461.0           7.0  28.920839   \n",
       "\n",
       "                                            genres  weighted_rating  \n",
       "1213                            [Horror, Thriller]              7.8  \n",
       "1176                     [Drama, Horror, Thriller]              7.8  \n",
       "1171   [Horror, Action, Thriller, Science Fiction]              7.6  \n",
       "1158   [Horror, Action, Thriller, Science Fiction]              7.4  \n",
       "2175            [Horror, Mystery, Science Fiction]              7.2  \n",
       "1338                 [Horror, Thriller, Adventure]              7.2  \n",
       "21276                           [Horror, Thriller]              7.1  \n",
       "8147                              [Horror, Comedy]              7.1  \n",
       "1888                     [Drama, Horror, Thriller]              7.1  \n",
       "14236                             [Comedy, Horror]              7.0  \n",
       "42169                  [Mystery, Thriller, Horror]              6.9  \n",
       "39821                   [Action, Horror, Thriller]              6.9  \n",
       "8230                      [Horror, Mystery, Crime]              6.9  \n",
       "4591            [Horror, Drama, Mystery, Thriller]              6.9  \n",
       "41492                           [Horror, Thriller]              6.8  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_recommender(N = 15, genre = 'Horror', percentile = 0.95)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d5f6f42",
   "metadata": {},
   "source": [
    "These are some top tier horror movies and any horror genre fan would be delighted to have these movies as recommendations. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a35ff8b9",
   "metadata": {},
   "source": [
    "## <font color='green'><a id='the_destination_8'>Section 3 - Content Based Recommender System</a></font>\n",
    "\n",
    "Now that we are done looking at the Simple Recommendation system, let's take a look at creating personalized recommendations with the help of Content Based Recommender. \n",
    "\n",
    "The recommender we built in the previous section had a broad appeal but suffers from the fact that it doesn't offer a personalized solution to a user. It gives the same recommendations to everyone, regardless of the user's personal taste. Thus, to personalize our recommendations, we can take relevant inputs like movie content, genre, etc which would give much better suggestions for a given user. Such a system is called as a Content Based Recommender.\n",
    "\n",
    "These systems make recommendations using a user's item and profile features. They hypothesize that if a user was interested in an item in the past, they will once again be interested in it in the future. Similar items are usually grouped based on their features. User profiles are constructed using historical interactions or by explicitly asking users about their interests. There are other systems, not considered purely content-based, which utilize user personal and social data.\n",
    "\n",
    "The thinking behind using these features is that if a user A likes the movie 'The Dark Knight', it is highly possible that he would be interested in other Batman movies/other Nolan movies/Other Bale movies. So in order to find such movies, we first extract the relevant information about the movie that we need (keywords, overview, cast, etc.), and use that information to find the similarity between our user liked movie and other movies in our list and recommend the movies that are highly similar to the user liked movie. \n",
    "    \n",
    "In this section, we will be building two Content Based Recommenders based on:\n",
    "\n",
    "Movie Overviews\\\n",
    "Movie Cast, Director, Keywords and Genre\n",
    "    \n",
    "#### <font color='green'>Steps Involved:</font>\n",
    "    \n",
    "1) The first step is to create a so-called ‘bag of words’ model from the input data, which is a list of words used to characterize the items. This step involves selecting useful content for describing and differentiating the items. The more precise the information, the better will be the recommendations\n",
    "2) The next step is to turn the bag (of words) into a feature vector. Different algorithms can be used for this step, for example, the Tfdif vectorizer or the count vectorizer. We will be using SBERT. The result is a vector matrix with items as records and features as columns. This step often also includes applying techniques for dimensionality reduction. But in our case we won't be doing that as SBERT already has a maximum word_embedding_dimension which restricts it to a preset limit (768 in our case). So dimensionality reduction won't be needed as the number of dimensions would already be small.\n",
    "3) The idea of content-based recommendations is based on measuring item similarity. Similarity scores are assigned through pairwise comparison. Here again, we have the choice between different measures, e.g., the dot product or cosine similarity\n",
    "4) Once you have the similarity scores, you can return the most similar items by sorting the data by similarity scores. Given user preferences (single or multiple items a user consumed or liked), the algorithm will then recommend the most similar items"
   ]
  },
  {
   "attachments": {
    "rsz_jenny_du_miword.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "ac08c0e9",
   "metadata": {},
   "source": [
    "![rsz_jenny_du_miword.png](attachment:rsz_jenny_du_miword.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caec97ac",
   "metadata": {},
   "source": [
    "Image source - https://www.tyrrell4innovation.ca/miword-of-the-day-iscosine-distance/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25676062",
   "metadata": {},
   "source": [
    "    \n",
    "#### <font color='green'>SBERT</font>\n",
    "\n",
    "We will be making use of SBERT (https://www.pinecone.io/learn/sentence-embeddings/#:~:text=Unlike%20BERT%2C%20SBERT%20is%20fine,sentence%20A%20and%20sentence%20B) to convert our content into vectors. \n",
    "The reason being that fine-tuned BERT classifiers outperform bag-of-words-based classifiers (https://link.springer.com/article/10.1007/s10660-022-09560-w). Most online material I have seen, have used TF-IDF to perform the vectorization. But since it's clear that BERT has better performance, we will be using BERT. Within BERT, we will be using the SBERT model 'all-mpnet-base-v2' as it has the best performance for sentence embeddings.\n",
    "    \n",
    "One thing to note is that, our SBERT model ('all-mpnet-base-v2') has a maximum sequence length of 384, which means that if our input is greater than 384, it will get truncated. But this shouldn't affect the recommendations too much.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b59b4f98",
   "metadata": {},
   "source": [
    "### <font color='green'><a id='the_destination_9'>3.1 Movie Overview Based Recommender</a></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7f0f0e8",
   "metadata": {},
   "source": [
    "Instead of taking the entire 46K movies for consideration, we will be taking only those movies in the top 95 percentile in terms of number of votes received for 2 reasons:\n",
    "\n",
    "1) Processing time will be faster (since we're running this on our personal computer rather than a production environment\n",
    "2) Most users prefer mainstream and popular movies that match their tastes for their suggestions than movies with very few ratings or appeal (keywords being 'most users' and not all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "24fce8bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2255, 8)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = round(md['vote_count'].quantile(0.95))  # You can change the percentiles to see how the result changes\n",
    "C = round(md['vote_average'].mean(), 1)\n",
    "    \n",
    "df = md[(md['vote_count'] >= m)][['id', 'title', 'year', 'vote_count', 'vote_average', 'popularity', 'genres', 'overview']]\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0926662c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e443191c3be48e78fd3c0b91abc634b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/71 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Need high processing power and would take ~30 mins to run for the entire 46K dataset. But in our case, for the 2,255 movies, it takes a couple of minutes\n",
    "\n",
    "text_data = np.array(df['overview'])\n",
    "model = SentenceTransformer('all-mpnet-base-v2') # All-round model tuned for many use-cases. Trained on a large and diverse dataset of over 1 billion training pairs.\n",
    "embeddings = model.encode(text_data, show_progress_bar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c23509a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method SentenceTransformer.get_sentence_features of SentenceTransformer(\n",
       "  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel \n",
       "  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n",
       "  (2): Normalize()\n",
       ")>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_sentence_features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b32423a",
   "metadata": {},
   "source": [
    "We can see that the model we chose has a max seq length of 384 and the word embedding dimension of 768."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dd999d6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "531"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Almost 1/4th of our data (531/2255) would get truncated as it exceeds the max_seq_length of 384. But it shouldn't affect the performance too much.\n",
    "\n",
    "i = [len(i) for i in text_data if len(i) > 384]\n",
    "len(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c233cb03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2255, 768)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4bdd8afe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Can take 15-20 mins to run if we use the entire 46K dataset. In our case of 2,255 movies, it takes a few secs\n",
    "\n",
    "cos_sim_data = pd.DataFrame(cosine_similarity(embeddings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6af333ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index()\n",
    "data = df[['title','genres','year']]\n",
    "indices = pd.Series(df.index, index=df['title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c7c44ceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_recommendations(title, N = 30):\n",
    "    idx = indices[title]\n",
    "    sim_scores = list(enumerate(cos_sim_data[idx]))\n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "    sim_scores = sim_scores[1:N+1]\n",
    "    movie_indices = [i[0] for i in sim_scores]\n",
    "    sim_scores = pd.DataFrame(sim_scores, columns = ['index', 'similarity_score']) \n",
    "    final_data = data.iloc[movie_indices]\n",
    "    final_data = final_data.merge(sim_scores, left_index = True, right_on ='index')\n",
    "    final_data['similarity_score'] = round(final_data['similarity_score']*100,2)\n",
    "    del final_data['index']\n",
    "    return final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6e6db023",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>similarity_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Godfather: Part III</td>\n",
       "      <td>[Crime, Drama, Thriller]</td>\n",
       "      <td>1990</td>\n",
       "      <td>70.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>1974</td>\n",
       "      <td>69.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Sting</td>\n",
       "      <td>[Comedy, Crime, Drama]</td>\n",
       "      <td>1973</td>\n",
       "      <td>60.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gangster Squad</td>\n",
       "      <td>[Crime, Drama, Action, Thriller]</td>\n",
       "      <td>2013</td>\n",
       "      <td>53.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mr. Holmes</td>\n",
       "      <td>[Drama, Mystery]</td>\n",
       "      <td>2015</td>\n",
       "      <td>52.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Black Mass</td>\n",
       "      <td>[Crime, Drama]</td>\n",
       "      <td>2015</td>\n",
       "      <td>50.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A Most Violent Year</td>\n",
       "      <td>[Crime, Drama, Thriller]</td>\n",
       "      <td>2014</td>\n",
       "      <td>50.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>American Hustle</td>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>2013</td>\n",
       "      <td>49.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miami Vice</td>\n",
       "      <td>[Action, Adventure, Crime, Thriller]</td>\n",
       "      <td>2006</td>\n",
       "      <td>49.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GoodFellas</td>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>1990</td>\n",
       "      <td>49.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A Bronx Tale</td>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>1993</td>\n",
       "      <td>48.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Donnie Brasco</td>\n",
       "      <td>[Crime, Drama, Thriller]</td>\n",
       "      <td>1997</td>\n",
       "      <td>48.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Once Upon a Time in America</td>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>1984</td>\n",
       "      <td>48.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sleepers</td>\n",
       "      <td>[Crime, Drama, Thriller]</td>\n",
       "      <td>1996</td>\n",
       "      <td>47.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bad Boys II</td>\n",
       "      <td>[Adventure, Action, Comedy, Thriller, Crime]</td>\n",
       "      <td>2003</td>\n",
       "      <td>47.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          title                                        genres  \\\n",
       "0       The Godfather: Part III                      [Crime, Drama, Thriller]   \n",
       "1        The Godfather: Part II                                [Drama, Crime]   \n",
       "2                     The Sting                        [Comedy, Crime, Drama]   \n",
       "3                Gangster Squad              [Crime, Drama, Action, Thriller]   \n",
       "4                    Mr. Holmes                              [Drama, Mystery]   \n",
       "5                    Black Mass                                [Crime, Drama]   \n",
       "6           A Most Violent Year                      [Crime, Drama, Thriller]   \n",
       "7               American Hustle                                [Drama, Crime]   \n",
       "8                    Miami Vice          [Action, Adventure, Crime, Thriller]   \n",
       "9                    GoodFellas                                [Drama, Crime]   \n",
       "10                 A Bronx Tale                                [Drama, Crime]   \n",
       "11                Donnie Brasco                      [Crime, Drama, Thriller]   \n",
       "12  Once Upon a Time in America                                [Drama, Crime]   \n",
       "13                     Sleepers                      [Crime, Drama, Thriller]   \n",
       "14                  Bad Boys II  [Adventure, Action, Comedy, Thriller, Crime]   \n",
       "\n",
       "    year  similarity_score  \n",
       "0   1990             70.66  \n",
       "1   1974             69.92  \n",
       "2   1973             60.85  \n",
       "3   2013             53.01  \n",
       "4   2015             52.37  \n",
       "5   2015             50.14  \n",
       "6   2014             50.11  \n",
       "7   2013             49.46  \n",
       "8   2006             49.28  \n",
       "9   1990             49.01  \n",
       "10  1993             48.93  \n",
       "11  1997             48.80  \n",
       "12  1984             48.16  \n",
       "13  1996             47.63  \n",
       "14  2003             47.51  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommendations('The Godfather', 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f208ad44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "      <th>similarity_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Closer</td>\n",
       "      <td>[Drama, Romance]</td>\n",
       "      <td>2004</td>\n",
       "      <td>68.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gone with the Wind</td>\n",
       "      <td>[Drama, Romance, War]</td>\n",
       "      <td>1939</td>\n",
       "      <td>67.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sleepless in Seattle</td>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>1993</td>\n",
       "      <td>66.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Before Sunrise</td>\n",
       "      <td>[Drama, Romance]</td>\n",
       "      <td>1995</td>\n",
       "      <td>66.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Moonlight</td>\n",
       "      <td>[Drama]</td>\n",
       "      <td>2016</td>\n",
       "      <td>64.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mystic River</td>\n",
       "      <td>[Thriller, Crime, Drama, Mystery]</td>\n",
       "      <td>2003</td>\n",
       "      <td>63.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Crimson Peak</td>\n",
       "      <td>[Mystery, Thriller, Fantasy, Horror, Drama]</td>\n",
       "      <td>2015</td>\n",
       "      <td>63.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Legends of the Fall</td>\n",
       "      <td>[Adventure, Drama, Romance, War]</td>\n",
       "      <td>1994</td>\n",
       "      <td>62.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cold Mountain</td>\n",
       "      <td>[Drama]</td>\n",
       "      <td>2003</td>\n",
       "      <td>62.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>The Visit</td>\n",
       "      <td>[Horror, Thriller]</td>\n",
       "      <td>2015</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Café Society</td>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>2016</td>\n",
       "      <td>61.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Love Actually</td>\n",
       "      <td>[Comedy, Romance, Drama]</td>\n",
       "      <td>2003</td>\n",
       "      <td>60.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Stuck in Love</td>\n",
       "      <td>[Drama, Comedy]</td>\n",
       "      <td>2012</td>\n",
       "      <td>59.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Collateral Beauty</td>\n",
       "      <td>[Drama, Romance]</td>\n",
       "      <td>2016</td>\n",
       "      <td>59.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>To Rome with Love</td>\n",
       "      <td>[Romance, Comedy]</td>\n",
       "      <td>2012</td>\n",
       "      <td>58.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   title                                       genres  year  \\\n",
       "0                 Closer                             [Drama, Romance]  2004   \n",
       "1     Gone with the Wind                        [Drama, Romance, War]  1939   \n",
       "2   Sleepless in Seattle                     [Comedy, Drama, Romance]  1993   \n",
       "3         Before Sunrise                             [Drama, Romance]  1995   \n",
       "4              Moonlight                                      [Drama]  2016   \n",
       "5           Mystic River            [Thriller, Crime, Drama, Mystery]  2003   \n",
       "6           Crimson Peak  [Mystery, Thriller, Fantasy, Horror, Drama]  2015   \n",
       "7    Legends of the Fall             [Adventure, Drama, Romance, War]  1994   \n",
       "8          Cold Mountain                                      [Drama]  2003   \n",
       "9              The Visit                           [Horror, Thriller]  2015   \n",
       "10          Café Society                     [Comedy, Drama, Romance]  2016   \n",
       "11         Love Actually                     [Comedy, Romance, Drama]  2003   \n",
       "12         Stuck in Love                              [Drama, Comedy]  2012   \n",
       "13     Collateral Beauty                             [Drama, Romance]  2016   \n",
       "14     To Rome with Love                            [Romance, Comedy]  2012   \n",
       "\n",
       "    similarity_score  \n",
       "0              68.53  \n",
       "1              67.46  \n",
       "2              66.73  \n",
       "3              66.32  \n",
       "4              64.76  \n",
       "5              63.70  \n",
       "6              63.15  \n",
       "7              62.75  \n",
       "8              62.56  \n",
       "9              62.00  \n",
       "10             61.44  \n",
       "11             60.84  \n",
       "12             59.97  \n",
       "13             59.25  \n",
       "14             58.95  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommendations('The Notebook', 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17ff842b",
   "metadata": {},
   "source": [
    "\n",
    "Despite truncating 1/4th of our input data, we're getting excellent recommendations for the chosen movies. But we can notice that the recommendations for 'The Godfather' still has some movies that aren't relevant like 'Bad Boys 2'\n",
    "\n",
    "Similarly, for the movie 'The Notebook', we can see movies like 'Mystic River', 'Crimson Peak' that don't have much in common. \n",
    "\n",
    "So, we need to further improve our technique. Let's make use of movie metadata other than description to improve our recommendations. We will be taking the following information to see if it improves our recommendations:\n",
    "- Genre\n",
    "- Keywords\n",
    "- Top 5 Cast\n",
    "- Director"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "513dc1c7",
   "metadata": {},
   "source": [
    "### <font color='green'><a id='the_destination_10'>3.2 Movie Metadata Based Recommender</a></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5bd0e42c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading in the credits and keywords data \n",
    "\n",
    "credits = pd.read_csv('C:/Users/balaj/Downloads/archive/credits.csv')\n",
    "keywords = pd.read_csv('C:/Users/balaj/Downloads/archive/keywords.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "da059383",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing duplicates from the data\n",
    "\n",
    "credits.drop_duplicates(inplace = True)\n",
    "keywords.drop_duplicates(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4641c4ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing the datatype of id to int to enable merging\n",
    "\n",
    "keywords['id'] = keywords['id'].astype('int')\n",
    "credits['id'] = credits['id'].astype('int')\n",
    "df['id'] = df['id'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "27768a65",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.merge(credits, on='id')\n",
    "df = df.merge(keywords, on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b38ea6cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2255, 12)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e330651b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>popularity</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>862</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>5415.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>21.946943</td>\n",
       "      <td>[Animation, Comedy, Family]</td>\n",
       "      <td>Led by Woody, Andy's toys live happily in his ...</td>\n",
       "      <td>[{'cast_id': 14, 'character': 'Woody (voice)',...</td>\n",
       "      <td>[{'credit_id': '52fe4284c3a36847f8024f49', 'de...</td>\n",
       "      <td>[{'id': 931, 'name': 'jealousy'}, {'id': 4290,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>8844</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>1995</td>\n",
       "      <td>2413.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>17.015539</td>\n",
       "      <td>[Adventure, Fantasy, Family]</td>\n",
       "      <td>When siblings Judy and Peter discover an encha...</td>\n",
       "      <td>[{'cast_id': 1, 'character': 'Alan Parrish', '...</td>\n",
       "      <td>[{'credit_id': '52fe44bfc3a36847f80a7cd1', 'de...</td>\n",
       "      <td>[{'id': 10090, 'name': 'board game'}, {'id': 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>949</td>\n",
       "      <td>Heat</td>\n",
       "      <td>1995</td>\n",
       "      <td>1886.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>17.924927</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>Obsessive master thief, Neil McCauley leads a ...</td>\n",
       "      <td>[{'cast_id': 25, 'character': 'Lt. Vincent Han...</td>\n",
       "      <td>[{'credit_id': '52fe4292c3a36847f802916d', 'de...</td>\n",
       "      <td>[{'id': 642, 'name': 'robbery'}, {'id': 703, '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>710</td>\n",
       "      <td>GoldenEye</td>\n",
       "      <td>1995</td>\n",
       "      <td>1194.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>14.686036</td>\n",
       "      <td>[Adventure, Action, Thriller]</td>\n",
       "      <td>James Bond must unmask the mysterious head of ...</td>\n",
       "      <td>[{'cast_id': 1, 'character': 'James Bond', 'cr...</td>\n",
       "      <td>[{'credit_id': '52fe426ec3a36847f801e14b', 'de...</td>\n",
       "      <td>[{'id': 701, 'name': 'cuba'}, {'id': 769, 'nam...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>524</td>\n",
       "      <td>Casino</td>\n",
       "      <td>1995</td>\n",
       "      <td>1343.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>10.137389</td>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>The life of the gambling paradise – Las Vegas ...</td>\n",
       "      <td>[{'cast_id': 4, 'character': \"Sam 'Ace' Rothst...</td>\n",
       "      <td>[{'credit_id': '52fe424dc3a36847f80139cd', 'de...</td>\n",
       "      <td>[{'id': 383, 'name': 'poker'}, {'id': 726, 'na...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index    id      title  year  vote_count  vote_average popularity  \\\n",
       "0      0   862  Toy Story  1995      5415.0           7.7  21.946943   \n",
       "1      1  8844    Jumanji  1995      2413.0           6.9  17.015539   \n",
       "2      5   949       Heat  1995      1886.0           7.7  17.924927   \n",
       "3      9   710  GoldenEye  1995      1194.0           6.6  14.686036   \n",
       "4     15   524     Casino  1995      1343.0           7.8  10.137389   \n",
       "\n",
       "                             genres  \\\n",
       "0       [Animation, Comedy, Family]   \n",
       "1      [Adventure, Fantasy, Family]   \n",
       "2  [Action, Crime, Drama, Thriller]   \n",
       "3     [Adventure, Action, Thriller]   \n",
       "4                    [Drama, Crime]   \n",
       "\n",
       "                                            overview  \\\n",
       "0  Led by Woody, Andy's toys live happily in his ...   \n",
       "1  When siblings Judy and Peter discover an encha...   \n",
       "2  Obsessive master thief, Neil McCauley leads a ...   \n",
       "3  James Bond must unmask the mysterious head of ...   \n",
       "4  The life of the gambling paradise – Las Vegas ...   \n",
       "\n",
       "                                                cast  \\\n",
       "0  [{'cast_id': 14, 'character': 'Woody (voice)',...   \n",
       "1  [{'cast_id': 1, 'character': 'Alan Parrish', '...   \n",
       "2  [{'cast_id': 25, 'character': 'Lt. Vincent Han...   \n",
       "3  [{'cast_id': 1, 'character': 'James Bond', 'cr...   \n",
       "4  [{'cast_id': 4, 'character': \"Sam 'Ace' Rothst...   \n",
       "\n",
       "                                                crew  \\\n",
       "0  [{'credit_id': '52fe4284c3a36847f8024f49', 'de...   \n",
       "1  [{'credit_id': '52fe44bfc3a36847f80a7cd1', 'de...   \n",
       "2  [{'credit_id': '52fe4292c3a36847f802916d', 'de...   \n",
       "3  [{'credit_id': '52fe426ec3a36847f801e14b', 'de...   \n",
       "4  [{'credit_id': '52fe424dc3a36847f80139cd', 'de...   \n",
       "\n",
       "                                            keywords  \n",
       "0  [{'id': 931, 'name': 'jealousy'}, {'id': 4290,...  \n",
       "1  [{'id': 10090, 'name': 'board game'}, {'id': 1...  \n",
       "2  [{'id': 642, 'name': 'robbery'}, {'id': 703, '...  \n",
       "3  [{'id': 701, 'name': 'cuba'}, {'id': 769, 'nam...  \n",
       "4  [{'id': 383, 'name': 'poker'}, {'id': 726, 'na...  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d8ecdc4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the cast names and keeping only the top 5 main cast in the list\n",
    "\n",
    "df['cast'] = df['cast'].apply(literal_eval).apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])\n",
    "df['cast'] = df['cast'].apply(lambda x: x[:5] if len(x) >=5 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d71d0924",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the keywords and storing it in a list\n",
    "\n",
    "df['keywords'] = df['keywords'].apply(literal_eval).apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ce12fac5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the director name \n",
    "\n",
    "def get_director(x):\n",
    "    for i in x:\n",
    "        if i['job'] == 'Director':\n",
    "            return i['name']\n",
    "    return np.nan\n",
    "\n",
    "df['director'] = df['crew'].apply(literal_eval).apply(get_director)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "49c4b171",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing space between names in order to differentiate between different actors and directors\n",
    "\n",
    "df['cast'] = df['cast'].apply(lambda x: [str.lower(i.replace(\" \", \"\")) for i in x])\n",
    "df['director'] = df['director'].astype('str').apply(lambda x: str.lower(x.replace(\" \", \"\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "79def54b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Changing the datatype to list to enable concatenation\n",
    "df['director'] = df['director'].apply(lambda x: [i for i in x.split()])\n",
    "\n",
    "# Creating a new column by concatenating all the relavant metadata info\n",
    "df['metadata'] = df['genres'] + df['keywords'] + df['director'] + df['cast']\n",
    "\n",
    "# Taking all the items in the metadata and joining it into a single string\n",
    "df['metadata'] = df['metadata'].apply(lambda x: ' '.join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "af7ac8ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b9cf41428c9d423a850bd41e4ed72ff3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/71 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "meta_data = np.array(df['metadata'])\n",
    "model = SentenceTransformer('all-mpnet-base-v2') # All-round model tuned for many use-cases. Trained on a large and diverse dataset of over 1 billion training pairs.\n",
    "embeddings = model.encode(meta_data, show_progress_bar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9815cbe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This time we're truncating only 46 datapoints out of 2255 which shouldn't affect the performance\n",
    "\n",
    "i = [len(i) for i in meta_data if len(i) > 384]\n",
    "len(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3d0c1787",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2255, 768)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "098cf921",
   "metadata": {},
   "outputs": [],
   "source": [
    "cos_sim_data = pd.DataFrame(cosine_similarity(embeddings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2ca1406f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "      <th>similarity_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>1974</td>\n",
       "      <td>71.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Bronx Tale</td>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>1993</td>\n",
       "      <td>68.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Analyze This</td>\n",
       "      <td>[Comedy, Crime]</td>\n",
       "      <td>1999</td>\n",
       "      <td>65.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Godfather: Part III</td>\n",
       "      <td>[Crime, Drama, Thriller]</td>\n",
       "      <td>1990</td>\n",
       "      <td>64.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cinema Paradiso</td>\n",
       "      <td>[Drama, Romance]</td>\n",
       "      <td>1988</td>\n",
       "      <td>62.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GoodFellas</td>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>1990</td>\n",
       "      <td>59.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>John Wick: Chapter 2</td>\n",
       "      <td>[Thriller, Action, Crime]</td>\n",
       "      <td>2017</td>\n",
       "      <td>59.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>The Family</td>\n",
       "      <td>[Crime, Comedy, Action]</td>\n",
       "      <td>2013</td>\n",
       "      <td>56.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A Walk Among the Tombstones</td>\n",
       "      <td>[Crime, Drama, Mystery, Thriller]</td>\n",
       "      <td>2014</td>\n",
       "      <td>55.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Meet the Fockers</td>\n",
       "      <td>[Comedy, Romance]</td>\n",
       "      <td>2004</td>\n",
       "      <td>54.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>The Sting</td>\n",
       "      <td>[Comedy, Crime, Drama]</td>\n",
       "      <td>1973</td>\n",
       "      <td>54.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A Fistful of Dollars</td>\n",
       "      <td>[Western]</td>\n",
       "      <td>1964</td>\n",
       "      <td>54.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>The Boondock Saints</td>\n",
       "      <td>[Action, Thriller, Crime]</td>\n",
       "      <td>1999</td>\n",
       "      <td>54.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>True Romance</td>\n",
       "      <td>[Action, Thriller, Crime, Romance]</td>\n",
       "      <td>1993</td>\n",
       "      <td>53.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Scarface</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>1983</td>\n",
       "      <td>53.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          title                              genres  year  \\\n",
       "0        The Godfather: Part II                      [Drama, Crime]  1974   \n",
       "1                  A Bronx Tale                      [Drama, Crime]  1993   \n",
       "2                  Analyze This                     [Comedy, Crime]  1999   \n",
       "3       The Godfather: Part III            [Crime, Drama, Thriller]  1990   \n",
       "4               Cinema Paradiso                    [Drama, Romance]  1988   \n",
       "5                    GoodFellas                      [Drama, Crime]  1990   \n",
       "6          John Wick: Chapter 2           [Thriller, Action, Crime]  2017   \n",
       "7                    The Family             [Crime, Comedy, Action]  2013   \n",
       "8   A Walk Among the Tombstones   [Crime, Drama, Mystery, Thriller]  2014   \n",
       "9              Meet the Fockers                   [Comedy, Romance]  2004   \n",
       "10                    The Sting              [Comedy, Crime, Drama]  1973   \n",
       "11         A Fistful of Dollars                           [Western]  1964   \n",
       "12          The Boondock Saints           [Action, Thriller, Crime]  1999   \n",
       "13                 True Romance  [Action, Thriller, Crime, Romance]  1993   \n",
       "14                     Scarface    [Action, Crime, Drama, Thriller]  1983   \n",
       "\n",
       "    similarity_score  \n",
       "0              71.20  \n",
       "1              68.31  \n",
       "2              65.09  \n",
       "3              64.47  \n",
       "4              62.64  \n",
       "5              59.97  \n",
       "6              59.28  \n",
       "7              56.40  \n",
       "8              55.55  \n",
       "9              54.84  \n",
       "10             54.56  \n",
       "11             54.47  \n",
       "12             54.14  \n",
       "13             53.36  \n",
       "14             53.27  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function already define in the previous section. So just reusing it. But re-running the pre-requisites for the function\n",
    "\n",
    "df = df.reset_index()\n",
    "data = df[['title','genres','year']]\n",
    "indices = pd.Series(df.index, index=df['title'])\n",
    "\n",
    "get_recommendations('The Godfather', 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6ea3eb1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "      <th>similarity_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>2012</td>\n",
       "      <td>92.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Batman: The Killing Joke</td>\n",
       "      <td>[Action, Animation, Crime, Drama]</td>\n",
       "      <td>2016</td>\n",
       "      <td>87.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Suicide Squad</td>\n",
       "      <td>[Action, Adventure, Crime, Fantasy, Science Fi...</td>\n",
       "      <td>2016</td>\n",
       "      <td>82.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Batman v Superman: Dawn of Justice</td>\n",
       "      <td>[Action, Adventure, Fantasy]</td>\n",
       "      <td>2016</td>\n",
       "      <td>80.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Batman Begins</td>\n",
       "      <td>[Action, Crime, Drama]</td>\n",
       "      <td>2005</td>\n",
       "      <td>79.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Batman: Under the Red Hood</td>\n",
       "      <td>[Action, Animation]</td>\n",
       "      <td>2010</td>\n",
       "      <td>79.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Batman Returns</td>\n",
       "      <td>[Action, Fantasy]</td>\n",
       "      <td>1992</td>\n",
       "      <td>79.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dredd</td>\n",
       "      <td>[Action, Science Fiction]</td>\n",
       "      <td>2012</td>\n",
       "      <td>79.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Superman</td>\n",
       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
       "      <td>1978</td>\n",
       "      <td>78.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Batman &amp; Robin</td>\n",
       "      <td>[Action, Crime, Fantasy]</td>\n",
       "      <td>1997</td>\n",
       "      <td>76.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Man of Steel</td>\n",
       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
       "      <td>2013</td>\n",
       "      <td>76.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Watchmen</td>\n",
       "      <td>[Action, Mystery, Science Fiction]</td>\n",
       "      <td>2009</td>\n",
       "      <td>76.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Fantastic Four</td>\n",
       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
       "      <td>2005</td>\n",
       "      <td>76.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>X-Men Origins: Wolverine</td>\n",
       "      <td>[Adventure, Action, Thriller, Science Fiction]</td>\n",
       "      <td>2009</td>\n",
       "      <td>76.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kick-Ass</td>\n",
       "      <td>[Action, Crime]</td>\n",
       "      <td>2010</td>\n",
       "      <td>75.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 title  \\\n",
       "0                The Dark Knight Rises   \n",
       "1             Batman: The Killing Joke   \n",
       "2                        Suicide Squad   \n",
       "3   Batman v Superman: Dawn of Justice   \n",
       "4                        Batman Begins   \n",
       "5           Batman: Under the Red Hood   \n",
       "6                       Batman Returns   \n",
       "7                                Dredd   \n",
       "8                             Superman   \n",
       "9                       Batman & Robin   \n",
       "10                        Man of Steel   \n",
       "11                            Watchmen   \n",
       "12                      Fantastic Four   \n",
       "13            X-Men Origins: Wolverine   \n",
       "14                            Kick-Ass   \n",
       "\n",
       "                                               genres  year  similarity_score  \n",
       "0                    [Action, Crime, Drama, Thriller]  2012             92.69  \n",
       "1                   [Action, Animation, Crime, Drama]  2016             87.03  \n",
       "2   [Action, Adventure, Crime, Fantasy, Science Fi...  2016             82.09  \n",
       "3                        [Action, Adventure, Fantasy]  2016             80.82  \n",
       "4                              [Action, Crime, Drama]  2005             79.81  \n",
       "5                                 [Action, Animation]  2010             79.66  \n",
       "6                                   [Action, Fantasy]  1992             79.24  \n",
       "7                           [Action, Science Fiction]  2012             79.06  \n",
       "8       [Action, Adventure, Fantasy, Science Fiction]  1978             78.43  \n",
       "9                            [Action, Crime, Fantasy]  1997             76.82  \n",
       "10      [Action, Adventure, Fantasy, Science Fiction]  2013             76.65  \n",
       "11                 [Action, Mystery, Science Fiction]  2009             76.48  \n",
       "12      [Action, Adventure, Fantasy, Science Fiction]  2005             76.07  \n",
       "13     [Adventure, Action, Thriller, Science Fiction]  2009             76.04  \n",
       "14                                    [Action, Crime]  2010             75.49  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommendations('The Dark Knight', 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48041ebb",
   "metadata": {},
   "source": [
    "As we can see now, the recommendations seem to make much more sense and are more in line with the given movie. We can further give better recommendations by combining our content based recommendations with our ratings, which you can find below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2a5becfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_recommendations_final(title, N = 30):\n",
    "    idx = indices[title]\n",
    "    sim_scores = list(enumerate(cos_sim_data[idx]))\n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "    sim_scores = sim_scores[1:N+1]\n",
    "    movie_indices = [i[0] for i in sim_scores]\n",
    "    sim_scores = pd.DataFrame(sim_scores, columns = ['index', 'similarity_score']) \n",
    "\n",
    "    movies = df.iloc[movie_indices][['title', 'genres', 'vote_count', 'vote_average', 'year']]\n",
    "    final_data = movies.merge(sim_scores, left_index = True, right_on ='index')\n",
    "    final_data['similarity_score'] = round(final_data['similarity_score']*100,2)\n",
    "    del final_data['index']\n",
    "    \n",
    "    m = round(df['vote_count'].quantile(0.95))\n",
    "    C = round(df['vote_average'].mean(), 1)\n",
    "    \n",
    "    def weighted_rating(x):\n",
    "        v = x['vote_count']\n",
    "        R = x['vote_average']\n",
    "        return round((v*R/(v+m)) + (m*C/(m+v)),1)\n",
    "    \n",
    "    final_data['weighted_rating'] = final_data.apply(weighted_rating, axis=1)\n",
    "    final_data.sort_values(['weighted_rating', 'year'], ascending= [False, False], inplace = True)\n",
    "    \n",
    "    return final_data.head(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6d44c72c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>year</th>\n",
       "      <th>similarity_score</th>\n",
       "      <th>weighted_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>9263.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>2012</td>\n",
       "      <td>92.69</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Batman Begins</td>\n",
       "      <td>[Action, Crime, Drama]</td>\n",
       "      <td>7511.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2005</td>\n",
       "      <td>79.81</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kick-Ass</td>\n",
       "      <td>[Action, Crime]</td>\n",
       "      <td>4747.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2010</td>\n",
       "      <td>75.49</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Batman: Under the Red Hood</td>\n",
       "      <td>[Action, Animation]</td>\n",
       "      <td>459.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>2010</td>\n",
       "      <td>79.66</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Watchmen</td>\n",
       "      <td>[Action, Mystery, Science Fiction]</td>\n",
       "      <td>2892.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2009</td>\n",
       "      <td>76.48</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Superman</td>\n",
       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
       "      <td>1042.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1978</td>\n",
       "      <td>78.43</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Batman: The Killing Joke</td>\n",
       "      <td>[Action, Animation, Crime, Drama]</td>\n",
       "      <td>485.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2016</td>\n",
       "      <td>87.03</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dredd</td>\n",
       "      <td>[Action, Science Fiction]</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2012</td>\n",
       "      <td>79.06</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Batman Returns</td>\n",
       "      <td>[Action, Fantasy]</td>\n",
       "      <td>1706.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>1992</td>\n",
       "      <td>79.24</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Man of Steel</td>\n",
       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
       "      <td>6462.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2013</td>\n",
       "      <td>76.65</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>X-Men Origins: Wolverine</td>\n",
       "      <td>[Adventure, Action, Thriller, Science Fiction]</td>\n",
       "      <td>4086.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2009</td>\n",
       "      <td>76.04</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Suicide Squad</td>\n",
       "      <td>[Action, Adventure, Crime, Fantasy, Science Fi...</td>\n",
       "      <td>7717.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2016</td>\n",
       "      <td>82.09</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Fantastic Four</td>\n",
       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
       "      <td>3040.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2005</td>\n",
       "      <td>76.07</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Batman v Superman: Dawn of Justice</td>\n",
       "      <td>[Action, Adventure, Fantasy]</td>\n",
       "      <td>7189.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2016</td>\n",
       "      <td>80.82</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Batman &amp; Robin</td>\n",
       "      <td>[Action, Crime, Fantasy]</td>\n",
       "      <td>1447.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1997</td>\n",
       "      <td>76.82</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 title  \\\n",
       "0                The Dark Knight Rises   \n",
       "4                        Batman Begins   \n",
       "14                            Kick-Ass   \n",
       "5           Batman: Under the Red Hood   \n",
       "11                            Watchmen   \n",
       "8                             Superman   \n",
       "1             Batman: The Killing Joke   \n",
       "7                                Dredd   \n",
       "6                       Batman Returns   \n",
       "10                        Man of Steel   \n",
       "13            X-Men Origins: Wolverine   \n",
       "2                        Suicide Squad   \n",
       "12                      Fantastic Four   \n",
       "3   Batman v Superman: Dawn of Justice   \n",
       "9                       Batman & Robin   \n",
       "\n",
       "                                               genres  vote_count  \\\n",
       "0                    [Action, Crime, Drama, Thriller]      9263.0   \n",
       "4                              [Action, Crime, Drama]      7511.0   \n",
       "14                                    [Action, Crime]      4747.0   \n",
       "5                                 [Action, Animation]       459.0   \n",
       "11                 [Action, Mystery, Science Fiction]      2892.0   \n",
       "8       [Action, Adventure, Fantasy, Science Fiction]      1042.0   \n",
       "1                   [Action, Animation, Crime, Drama]       485.0   \n",
       "7                           [Action, Science Fiction]      1971.0   \n",
       "6                                   [Action, Fantasy]      1706.0   \n",
       "10      [Action, Adventure, Fantasy, Science Fiction]      6462.0   \n",
       "13     [Adventure, Action, Thriller, Science Fiction]      4086.0   \n",
       "2   [Action, Adventure, Crime, Fantasy, Science Fi...      7717.0   \n",
       "12      [Action, Adventure, Fantasy, Science Fiction]      3040.0   \n",
       "3                        [Action, Adventure, Fantasy]      7189.0   \n",
       "9                            [Action, Crime, Fantasy]      1447.0   \n",
       "\n",
       "    vote_average  year  similarity_score  weighted_rating  \n",
       "0            7.6  2012             92.69              7.3  \n",
       "4            7.5  2005             79.81              7.1  \n",
       "14           7.1  2010             75.49              6.8  \n",
       "5            7.6  2010             79.66              6.7  \n",
       "11           7.0  2009             76.48              6.7  \n",
       "8            6.9  1978             78.43              6.7  \n",
       "1            6.2  2016             87.03              6.6  \n",
       "7            6.6  2012             79.06              6.6  \n",
       "6            6.6  1992             79.24              6.6  \n",
       "10           6.5  2013             76.65              6.5  \n",
       "13           6.2  2009             76.04              6.4  \n",
       "2            5.9  2016             82.09              6.2  \n",
       "12           5.5  2005             76.07              6.2  \n",
       "3            5.7  2016             80.82              6.1  \n",
       "9            4.2  1997             76.82              6.0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommendations_final('The Dark Knight', 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4461a3e0",
   "metadata": {},
   "source": [
    "Based on these new recommendations, it's clear that this looks like a better set of suggestions which incorporates both content based metadata as well as the ratings of the movies. \n",
    "\n",
    "But one thing you can notice is that, in the example above, we recommended movies based on a user's preference of a single movie. In reality, during recommendation, the user would have watched a lot of different movies and hence would have a varied taste. Let's create a recommendation system for this scenario."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "13510295",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>similarity_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>Spectre</td>\n",
       "      <td>2015</td>\n",
       "      <td>163.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>From Russia with Love</td>\n",
       "      <td>1963</td>\n",
       "      <td>162.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>Tomorrow Never Dies</td>\n",
       "      <td>1997</td>\n",
       "      <td>156.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>The November Man</td>\n",
       "      <td>2014</td>\n",
       "      <td>155.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>Atomic Blonde</td>\n",
       "      <td>2017</td>\n",
       "      <td>155.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1356</th>\n",
       "      <td>Salt</td>\n",
       "      <td>2010</td>\n",
       "      <td>152.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>On Her Majesty's Secret Service</td>\n",
       "      <td>1969</td>\n",
       "      <td>151.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>The World Is Not Enough</td>\n",
       "      <td>1999</td>\n",
       "      <td>150.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>La Femme Nikita</td>\n",
       "      <td>1990</td>\n",
       "      <td>149.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>Bolt</td>\n",
       "      <td>2008</td>\n",
       "      <td>148.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2133</th>\n",
       "      <td>Wanted</td>\n",
       "      <td>2008</td>\n",
       "      <td>148.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1549</th>\n",
       "      <td>Taken 2</td>\n",
       "      <td>2012</td>\n",
       "      <td>146.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1522</th>\n",
       "      <td>Stuart Little 2</td>\n",
       "      <td>2002</td>\n",
       "      <td>146.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051</th>\n",
       "      <td>Mission: Impossible</td>\n",
       "      <td>1996</td>\n",
       "      <td>145.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>Dr. No</td>\n",
       "      <td>1962</td>\n",
       "      <td>84.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                title  year  similarity_score\n",
       "1469                          Spectre  2015            163.67\n",
       "611             From Russia with Love  1963            162.27\n",
       "2050              Tomorrow Never Dies  1997            156.71\n",
       "1891                 The November Man  2014            155.87\n",
       "161                     Atomic Blonde  2017            155.45\n",
       "1356                             Salt  2010            152.12\n",
       "1147  On Her Majesty's Secret Service  1969            151.17\n",
       "2024          The World Is Not Enough  1999            150.12\n",
       "910                   La Femme Nikita  1990            149.23\n",
       "266                              Bolt  2008            148.80\n",
       "2133                           Wanted  2008            148.34\n",
       "1549                          Taken 2  2012            146.56\n",
       "1522                  Stuart Little 2  2002            146.00\n",
       "1051              Mission: Impossible  1996            145.99\n",
       "478                            Dr. No  1962             84.47"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_list = ['The Lion King', 'Se7en', 'Toy Story', 'Blade Runner', 'Quantum of Solace', 'Casino Royale', 'Skyfall']\n",
    "\n",
    "# Getting the top N movies of each of the movies watched by our user and then summing the similarity score and sorting them\n",
    "\n",
    "user_scores = pd.DataFrame(df[['title', 'year', 'genres']])\n",
    "user_scores['similarity_score'] = 0.0\n",
    "N = 15\n",
    "\n",
    "for movie_name in movie_list:\n",
    "    top_titles_df = get_recommendations(movie_name, N)\n",
    "    user_scores = pd.concat([user_scores, top_titles_df[['title', 'similarity_score']]]).groupby(['title'], as_index=False).sum({'similarity_score'})\n",
    "\n",
    "# Sort and print the aggregated scores\n",
    "user_scores.sort_values(by='similarity_score', ascending=False)[:N]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "904e019d",
   "metadata": {},
   "source": [
    "This final list of recommendation definitely covers most Bond movies/Action movies and also has a few animated movies which resembles our user's watched movies list. Hopefully, this gives you a good idea about Content Based Recommendation systems. Finally, let's look at the pros and cons of Content based systems:\n",
    "\n",
    "<b> Pros: </b>\n",
    "- Content-based filtering does not require other users' data during recommendations to one user\n",
    "- Can provide explanations of recommended items by listing content-features\tthat caused\tan item\tto be recommended\n",
    "\n",
    "<b> Cons: </b>\n",
    "- One issue that is clearly visible and which generally arises in Content based systems is making obvious recommendations because of over-specialization (user A is only interested in categories B, C, and D, and the system is not able to recommend items outside those categories, even though they could be interesting to them)\n",
    "- It is only capable of suggesting movies which are close to a certain movie/genre based on the user's watching history. It is not capable of capturing tastes and providing recommendations across genres\n",
    "- Hard to give recommendations for new users\n",
    "\n",
    "Now that we have seen how Content Based Recommender systems work, let's turn our attention towards Collaborative Filtering system. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dddbe62b",
   "metadata": {},
   "source": [
    "## <font color='green'><a id='the_destination_11'>Section 4 - Collaborative Filtering System</a></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8c4fdce",
   "metadata": {},
   "source": [
    "Collaborative filtering is currently one of the most frequently used approaches and usually provides better results than content-based recommendations. Some examples of this are found in the recommendation systems of Youtube, Netflix, and Spotify.\n",
    "\n",
    "These kinds of systems utilize user interactions to filter for items of interest. We can visualize the set of interactions with a matrix, where each entry (i, j)(i,j) represents the interaction between user ii and item jj. An interesting way of looking at collaborative filtering is to think of it as a generalization of classification and regression. While in these cases we aim to predict a variable that directly depends on other variables (features), in collaborative filtering there is no such distinction of feature variables and class variables.\n",
    "\n",
    "Visualizing the problem as a matrix, we don’t look to predict the values of a unique column, but rather to predict the value of any given entry.\n",
    "\n",
    "In short, collaborative filtering systems are based on the assumption that if a user likes item A and another user likes the same item A as well as another item, item B, the first user could also be interested in the second item. Hence, they aim to predict new interactions based on historical ones. There are two types of methods to achieve this goal: memory-based and model-based.\n",
    "\n",
    "### <font color='green'>Memory-based</font>\n",
    "There are two approaches: the first one identifies clusters of users and utilizes the interactions of one specific user compared to the interactions of other similar users to create recommendations (<b>User-Based</b>). The second approach identifies clusters of items that are similar to a given item and recommends the top items that resemble the item rated by a particular user (<b>Item-Based</b>). These methods usually encounter major problems with large sparse matrices, since the number of user-item interactions can be too low for generating high quality clusters.\n",
    "\n",
    "To summarize -\n",
    "\n",
    "User Based Collaborative Filtering: “Users who are similar to you also liked\"\\\n",
    "Item Based Collaborative Filtering: “Users who liked this item also liked”\n",
    "\n",
    "### <font color='green'>Model-based</font>\n",
    "These methods are based on machine learning and data mining techniques. The goal is to train models to be able to make predictions. For example, we could use existing user-item interactions to train a model to predict the top-5 items that a user might like the most. One advantage of these methods is that they are able to recommend a larger number of items to a larger number of users, compared to other methods like memory-based. We say they have large coverage, even when working with large sparse matrices."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf2b1733",
   "metadata": {},
   "source": [
    "## <font color='green'><a id='the_destination_12'>4.1 Memory-Based Systems</a></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fd7c163",
   "metadata": {},
   "source": [
    "### <font color='green'><a id='the_destination_13'>4.1.1 User-Based Collaborative Filtering</a></font>\n",
    "\n",
    "User-based collaborative filtering algorithm usually has the following steps:\n",
    "\n",
    "1) Find similar users based on interactions with common items\n",
    "2) Identify the items rated high by similar users but have not been exposed to the active user of interest\n",
    "3) Calculate the weighted average score for each item\n",
    "4) Rank items based on the score and pick the top n items to recommend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4aeace60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
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       "      <th>0</th>\n",
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       "      <td>2.5</td>\n",
       "      <td>1260759144</td>\n",
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       "      <th>2</th>\n",
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       "      <td>1061</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1129</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1172</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1260759205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1       31     2.5  1260759144\n",
       "1       1     1029     3.0  1260759179\n",
       "2       1     1061     3.0  1260759182\n",
       "3       1     1129     2.0  1260759185\n",
       "4       1     1172     4.0  1260759205"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading user level data. We are loading just a small subset of the user ratings data. The actual data is 700 MB large and hence would take a long time to run our analyses\n",
    "\n",
    "user_data = pd.read_csv('C:/Users/balaj/Downloads/archive/ratings_small.csv')\n",
    "user_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "16908c1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100004, 4)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "722367d5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "671"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data['userId'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "01d87a31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9066"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data['movieId'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a5392d10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(user_data['rating'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d85b687",
   "metadata": {},
   "source": [
    "We can see that there are a total of 100K ratings records from 671 users for 9,066 movies. The ratings range from 0.5 - 5. In order to bring more context to the data, let's merge our user data with the movie data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a0a2be7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merging the user data with movie data\n",
    "\n",
    "user_data['movieId'] = user_data['movieId'].astype(int)\n",
    "md['id'] = md['id'].astype(int)\n",
    "user_data = user_data.merge(md[['title', 'genres', 'year', 'id']], how = 'inner', left_on = 'movieId', right_on = 'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "650572a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
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       "      <th>id</th>\n",
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       "    <tr>\n",
       "      <th>44934</th>\n",
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       "      <td>1442690827</td>\n",
       "      <td>Love Is a Ball</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>[Drama, Science Fiction, Mystery, Romance]</td>\n",
       "      <td>2002</td>\n",
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       "      <td>167</td>\n",
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       "      <th>44937</th>\n",
       "      <td>659</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>Starship Troopers</td>\n",
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       "      <td>1997</td>\n",
       "      <td>563</td>\n",
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       "      <td>995232528</td>\n",
       "      <td>Spirited Away</td>\n",
       "      <td>[Fantasy, Adventure, Animation, Family]</td>\n",
       "      <td>2001</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
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       "       userId  movieId  rating   timestamp              title  \\\n",
       "44934     652   129009     4.0  1442690827     Love Is a Ball   \n",
       "44935     653     2103     3.0   948161066            Solaris   \n",
       "44936     659      167     4.0   836137550              K-PAX   \n",
       "44937     659      563     3.0   834694187  Starship Troopers   \n",
       "44938     665      129     3.0   995232528      Spirited Away   \n",
       "\n",
       "                                               genres  year      id  \n",
       "44934                               [Comedy, Romance]  1963  129009  \n",
       "44935      [Drama, Science Fiction, Mystery, Romance]  2002    2103  \n",
       "44936                        [Drama, Science Fiction]  2001     167  \n",
       "44937  [Adventure, Action, Thriller, Science Fiction]  1997     563  \n",
       "44938         [Fantasy, Adventure, Animation, Family]  2001     129  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e302f413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(44939, 8)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4205893c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2817"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data['movieId'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2c70197f",
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_ratings = user_data.groupby('title').agg(mean_rating = ('rating', 'mean'), number_of_ratings = ('rating', 'count')).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "9e07c267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <td>!Women Art Revolution</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>'Gator Bait</td>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>...And God Created Woman</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00 Schneider - Jagd auf Nihil Baxter</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  title  mean_rating  number_of_ratings\n",
       "0                 !Women Art Revolution         3.25                  2\n",
       "1                           'Gator Bait         0.50                  1\n",
       "2      'Twas the Night Before Christmas         4.00                  2\n",
       "3              ...And God Created Woman         4.00                  1\n",
       "4  00 Schneider - Jagd auf Nihil Baxter         4.00                  2"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67a90cf1",
   "metadata": {},
   "source": [
    "We can clearly see that there are a lot of movies where only 1 person has rated. So, we need to filter and keep only those movies with over a minimum of 95 percentile number of ratings for the analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "bb522a5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val = round(agg_ratings['number_of_ratings'].quantile(0.95))\n",
    "val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "16c0785e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(139, 3)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering for movies with over 71 ratings\n",
    "\n",
    "agg_ratings_final = agg_ratings[agg_ratings['number_of_ratings'] > val]\n",
    "agg_ratings_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0857b807",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>Terminator 3: Rise of the Machines</td>\n",
       "      <td>4.256173</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2297</th>\n",
       "      <td>The Million Dollar Hotel</td>\n",
       "      <td>4.487138</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1831</th>\n",
       "      <td>Solaris</td>\n",
       "      <td>4.134426</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>The 39 Steps</td>\n",
       "      <td>4.221649</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1370</th>\n",
       "      <td>Monsoon Wedding</td>\n",
       "      <td>3.706204</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   title  mean_rating  number_of_ratings\n",
       "1952  Terminator 3: Rise of the Machines     4.256173                324\n",
       "2297            The Million Dollar Hotel     4.487138                311\n",
       "1831                             Solaris     4.134426                305\n",
       "1960                        The 39 Steps     4.221649                291\n",
       "1370                     Monsoon Wedding     3.706204                274"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_ratings_final.sort_values(by='number_of_ratings', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e6c8ce94",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x13c6f5dc850>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='mean_rating', y='number_of_ratings', data=agg_ratings_final)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91b47379",
   "metadata": {},
   "source": [
    "After applying the filters, we end up with 139 movies. Plotting the avg ratings vs the number of ratings, we can see that most movies have less than 100 ratings. You can also see a slightly positive correlation between the two.\n",
    "\n",
    "Let's join the agg_ratings_final dataset with the user_data and proceed further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c15ff0e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17629, 8)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data_final = pd.merge(user_data, agg_ratings_final[['title']], on='title', how='inner')\n",
    "user_data_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "bc620b24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "658"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data_final['userId'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5df9c863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>5.0</td>\n",
       "      <td>835355681</td>\n",
       "      <td>The Dark</td>\n",
       "      <td>[Horror, Thriller, Mystery]</td>\n",
       "      <td>2006</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>4.0</td>\n",
       "      <td>938628337</td>\n",
       "      <td>The Dark</td>\n",
       "      <td>[Horror, Thriller, Mystery]</td>\n",
       "      <td>2006</td>\n",
       "      <td>17</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>3.0</td>\n",
       "      <td>997939404</td>\n",
       "      <td>The Dark</td>\n",
       "      <td>[Horror, Thriller, Mystery]</td>\n",
       "      <td>2006</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>4.0</td>\n",
       "      <td>856006886</td>\n",
       "      <td>The Dark</td>\n",
       "      <td>[Horror, Thriller, Mystery]</td>\n",
       "      <td>2006</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44</td>\n",
       "      <td>17</td>\n",
       "      <td>2.0</td>\n",
       "      <td>858707139</td>\n",
       "      <td>The Dark</td>\n",
       "      <td>[Horror, Thriller, Mystery]</td>\n",
       "      <td>2006</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp     title                       genres  \\\n",
       "0       2       17     5.0  835355681  The Dark  [Horror, Thriller, Mystery]   \n",
       "1       9       17     4.0  938628337  The Dark  [Horror, Thriller, Mystery]   \n",
       "2      15       17     3.0  997939404  The Dark  [Horror, Thriller, Mystery]   \n",
       "3      18       17     4.0  856006886  The Dark  [Horror, Thriller, Mystery]   \n",
       "4      44       17     2.0  858707139  The Dark  [Horror, Thriller, Mystery]   \n",
       "\n",
       "   year  id  \n",
       "0  2006  17  \n",
       "1  2006  17  \n",
       "2  2006  17  \n",
       "3  2006  17  \n",
       "4  2006  17  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74eed3df",
   "metadata": {},
   "source": [
    "After filtering the movies with over 71 ratings, we have 658 users that rated 139 movies. Now, we can transform the dataset into a matrix format. The rows of the matrix are users, and the columns of the matrix are movies. The value of the matrix is the user rating of the movie if there is a rating. Otherwise, it shows ‘NaN’."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "25635a0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>title</th>\n",
       "      <th>20,000 Leagues Under the Sea</th>\n",
       "      <th>2001: A Space Odyssey</th>\n",
       "      <th>48 Hrs.</th>\n",
       "      <th>5 Card Stud</th>\n",
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       "      <th>Aliens vs Predator: Requiem</th>\n",
       "      <th>All the Way Boys</th>\n",
       "      <th>...</th>\n",
       "      <th>To Kill a Mockingbird</th>\n",
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       "      <th>Twin Peaks: Fire Walk with Me</th>\n",
       "      <th>Under the Sand</th>\n",
       "      <th>Wag the Dog</th>\n",
       "      <th>Waiter</th>\n",
       "      <th>Who Killed Bambi?</th>\n",
       "      <th>Will Penny</th>\n",
       "      <th>Y Tu Mamá También</th>\n",
       "      <th>Young and Innocent</th>\n",
       "    </tr>\n",
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       "      <th>userId</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 139 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title   20,000 Leagues Under the Sea  2001: A Space Odyssey  48 Hrs.  \\\n",
       "userId                                                                 \n",
       "2                                NaN                    3.0      5.0   \n",
       "3                                NaN                    NaN      NaN   \n",
       "4                                3.0                    NaN      NaN   \n",
       "5                                NaN                    NaN      4.0   \n",
       "6                                2.0                    NaN      NaN   \n",
       "\n",
       "title   5 Card Stud  A Clockwork Orange  A Nightmare on Elm Street  \\\n",
       "userId                                                               \n",
       "2               NaN                 3.0                        3.0   \n",
       "3               NaN                 NaN                        2.5   \n",
       "4               NaN                 3.0                        NaN   \n",
       "5               NaN                 NaN                        4.0   \n",
       "6               NaN                 NaN                        NaN   \n",
       "\n",
       "title   A River Runs Through It  Aguirre: The Wrath of God  \\\n",
       "userId                                                       \n",
       "2                           NaN                        NaN   \n",
       "3                           NaN                        NaN   \n",
       "4                           NaN                        5.0   \n",
       "5                           NaN                        NaN   \n",
       "6                           5.0                        NaN   \n",
       "\n",
       "title   Aliens vs Predator: Requiem  All the Way Boys  ...  \\\n",
       "userId                                                 ...   \n",
       "2                               NaN               NaN  ...   \n",
       "3                               NaN               4.5  ...   \n",
       "4                               4.0               NaN  ...   \n",
       "5                               4.0               4.0  ...   \n",
       "6                               NaN               NaN  ...   \n",
       "\n",
       "title   To Kill a Mockingbird  Tough Enough  Twin Peaks: Fire Walk with Me  \\\n",
       "userId                                                                       \n",
       "2                         NaN           NaN                            NaN   \n",
       "3                         2.0           NaN                            NaN   \n",
       "4                         NaN           4.0                            NaN   \n",
       "5                         4.0           NaN                            4.5   \n",
       "6                         NaN           NaN                            NaN   \n",
       "\n",
       "title   Under the Sand  Wag the Dog  Waiter  Who Killed Bambi?  Will Penny  \\\n",
       "userId                                                                       \n",
       "2                  NaN          3.0     NaN                NaN         NaN   \n",
       "3                  NaN          NaN     NaN                NaN         NaN   \n",
       "4                  NaN          NaN     NaN                4.0         NaN   \n",
       "5                  NaN          4.0     3.0                NaN         NaN   \n",
       "6                  NaN          NaN     NaN                NaN         NaN   \n",
       "\n",
       "title   Y Tu Mamá También  Young and Innocent  \n",
       "userId                                         \n",
       "2                     NaN                 NaN  \n",
       "3                     NaN                 3.5  \n",
       "4                     NaN                 NaN  \n",
       "5                     NaN                 3.5  \n",
       "6                     NaN                 NaN  \n",
       "\n",
       "[5 rows x 139 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = user_data_final.pivot_table(index='userId', columns='title', values='rating')\n",
    "matrix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7028b926",
   "metadata": {},
   "source": [
    "Since some people tend to give a higher rating than others, we perform something called as mean-centering where we subtract each rating of a user with their average rating. (null values are not included)\n",
    "\n",
    "After mean-centering, the movies with a rating less than the user’s average rating get a negative value, and the movies with a rating more than the user’s average rating get a positive value thereby effectively differentiating users' preferences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d94bcec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>title</th>\n",
       "      <th>20,000 Leagues Under the Sea</th>\n",
       "      <th>2001: A Space Odyssey</th>\n",
       "      <th>48 Hrs.</th>\n",
       "      <th>5 Card Stud</th>\n",
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       "      <th>A River Runs Through It</th>\n",
       "      <th>Aguirre: The Wrath of God</th>\n",
       "      <th>Aliens vs Predator: Requiem</th>\n",
       "      <th>All the Way Boys</th>\n",
       "      <th>...</th>\n",
       "      <th>To Kill a Mockingbird</th>\n",
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       "      <th>Under the Sand</th>\n",
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       "      <th>Will Penny</th>\n",
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       "      <th>Young and Innocent</th>\n",
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       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>1.4</td>\n",
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       "      <td>-0.60</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>-0.6</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.91</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.50</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 139 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title   20,000 Leagues Under the Sea  2001: A Space Odyssey  48 Hrs.  \\\n",
       "userId                                                                 \n",
       "2                               0.00                   -0.6      1.4   \n",
       "3                               0.00                    0.0      0.0   \n",
       "4                              -1.46                    0.0      0.0   \n",
       "5                               0.00                    0.0      0.1   \n",
       "6                              -1.50                    0.0      0.0   \n",
       "\n",
       "title   5 Card Stud  A Clockwork Orange  A Nightmare on Elm Street  \\\n",
       "userId                                                               \n",
       "2               0.0               -0.60                      -0.60   \n",
       "3               0.0                0.00                      -1.09   \n",
       "4               0.0               -1.46                       0.00   \n",
       "5               0.0                0.00                       0.10   \n",
       "6               0.0                0.00                       0.00   \n",
       "\n",
       "title   A River Runs Through It  Aguirre: The Wrath of God  \\\n",
       "userId                                                       \n",
       "2                           0.0                       0.00   \n",
       "3                           0.0                       0.00   \n",
       "4                           0.0                       0.54   \n",
       "5                           0.0                       0.00   \n",
       "6                           1.5                       0.00   \n",
       "\n",
       "title   Aliens vs Predator: Requiem  All the Way Boys  ...  \\\n",
       "userId                                                 ...   \n",
       "2                              0.00              0.00  ...   \n",
       "3                              0.00              0.91  ...   \n",
       "4                             -0.46              0.00  ...   \n",
       "5                              0.10              0.10  ...   \n",
       "6                              0.00              0.00  ...   \n",
       "\n",
       "title   To Kill a Mockingbird  Tough Enough  Twin Peaks: Fire Walk with Me  \\\n",
       "userId                                                                       \n",
       "2                        0.00          0.00                            0.0   \n",
       "3                       -1.59          0.00                            0.0   \n",
       "4                        0.00         -0.46                            0.0   \n",
       "5                        0.10          0.00                            0.6   \n",
       "6                        0.00          0.00                            0.0   \n",
       "\n",
       "title   Under the Sand  Wag the Dog  Waiter  Who Killed Bambi?  Will Penny  \\\n",
       "userId                                                                       \n",
       "2                  0.0         -0.6     0.0               0.00         0.0   \n",
       "3                  0.0          0.0     0.0               0.00         0.0   \n",
       "4                  0.0          0.0     0.0              -0.46         0.0   \n",
       "5                  0.0          0.1    -0.9               0.00         0.0   \n",
       "6                  0.0          0.0     0.0               0.00         0.0   \n",
       "\n",
       "title   Y Tu Mamá También  Young and Innocent  \n",
       "userId                                         \n",
       "2                     0.0                0.00  \n",
       "3                     0.0               -0.09  \n",
       "4                     0.0                0.00  \n",
       "5                     0.0               -0.40  \n",
       "6                     0.0                0.00  \n",
       "\n",
       "[5 rows x 139 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_norm = round(matrix.subtract(matrix.mean(axis=1), axis = 'rows'),2)\n",
    "matrix_norm = matrix_norm.fillna(0)\n",
    "matrix_norm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c4319b",
   "metadata": {},
   "source": [
    "There are different ways to measure similarities. Pearson correlation and Cosine similarity are two widely used methods. Let's continue to use cosine_similarity. Since cosine_similarity does not take missing values, we need to impute the missing values with 0s before the calculation (which we have done above).\n",
    "\n",
    "Let's write a function to find the K nearest users to a given user using cosine similarity. We can give 5 as the default value for K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3b450d54",
   "metadata": {},
   "outputs": [],
   "source": [
    "def similar_users_fn(user_id, matrix, K=5):\n",
    "    \n",
    "    # Create a df of just the current user\n",
    "    user = matrix[matrix.index == user_id]\n",
    "    \n",
    "    # Create a df of all other users\n",
    "    other_users = matrix[matrix.index != user_id]\n",
    "    \n",
    "    # Get cosine similarity between user and each of the other users\n",
    "    similarities = cosine_similarity(user, other_users)[0].tolist()\n",
    "    \n",
    "    # Create list of ids of these users\n",
    "    ids = other_users.index.tolist()\n",
    "    \n",
    "    # Create key/values pairs of user id and their similarity\n",
    "    id_similarity = dict(zip(ids, similarities))\n",
    "    \n",
    "    # Sort by similarity (desc)\n",
    "    id_similarity_sorted = [(k, v) for k, v in sorted(id_similarity.items(), key=lambda item: item[1], reverse = True)]\n",
    "    \n",
    "    # Grab k users off the top\n",
    "    users = [u for u in id_similarity_sorted[:K]]\n",
    "    \n",
    "    return users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d5951dca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(3, 0.3852231156101469),\n",
       " (172, 0.3780694011718894),\n",
       " (590, 0.3550867328698299),\n",
       " (24, 0.352145671884849),\n",
       " (267, 0.3029029187104928)]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user = 39\n",
    "similar_user_ids = similar_users_fn(user, matrix_norm)\n",
    "similar_user_ids"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ded59e4",
   "metadata": {},
   "source": [
    "Finally, let's write a function to give the recommendations. We can once again set the function to return the 5 top recommended movies by default. Here we will be using the weighted average to calculate the ratings of each movie so that users who have high similarity score to the given user have a higher weight on ratings. Then we pick only those movies which our user hasn't watched, sort them by the weighted average and suggest them as recommendations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0ce54532",
   "metadata": {},
   "outputs": [],
   "source": [
    "def user_based_recommender(user_id, matrix, items=5, users=5):\n",
    "    \n",
    "    similar_user_ids_and_ratings = pd.DataFrame(similar_users_fn(user_id, matrix, users), columns = ['similar_user_ids', 'similarity_score'])\n",
    "    \n",
    "    # Load vectors for similar users\n",
    "    similar_users = matrix[matrix.index.isin(similar_user_ids_and_ratings['similar_user_ids'])]\n",
    "    similar_users = similar_users.merge(similar_user_ids_and_ratings, left_on = 'userId', right_on = 'similar_user_ids')\n",
    "    \n",
    "    # Calc weighted avg ratings for movies across the N similar users\n",
    "    cols = similar_users.columns[:-2]\n",
    "    final_ratings = {}\n",
    "    for movie in cols:\n",
    "        final_ratings[movie] = round(np.average(similar_users[movie], axis = 0, weights = similar_users['similarity_score']),2)\n",
    "    \n",
    "    # Convert to dataframe so its easy to sort and filter\n",
    "    similar_users_df = pd.DataFrame(final_ratings.items(), columns = ['title', 'weighted_average'])\n",
    "      \n",
    "    # Load vector for the current user\n",
    "    user_df = matrix[matrix.index == user_id]\n",
    "    \n",
    "    # Transpose it so its easier to filter\n",
    "    user_df_transposed = user_df.transpose()\n",
    "    \n",
    "    # Rename the column as 'rating'\n",
    "    user_df_transposed.columns = ['rating']\n",
    "    \n",
    "    # Get only those movies with rating as 0 since the user has not watched them yet\n",
    "    user_df_transposed = user_df_transposed[user_df_transposed['rating']==0]\n",
    "    \n",
    "    # Convert it into a list which contains movies the user has not seen\n",
    "    movies_unseen = user_df_transposed.index.tolist()\n",
    "    \n",
    "    # Filter weighted avg ratings given by similar users only for those movies the current user has not seen\n",
    "    similar_users_filtered = similar_users_df[similar_users_df['title'].isin(movies_unseen)]\n",
    "    \n",
    "    # Order the dataframe by highest rated movies\n",
    "    similar_users_df_ordered = similar_users_filtered.sort_values(by=['weighted_average'], ascending=False)\n",
    "    \n",
    "#     # Grab the top n movies   \n",
    "#     top_n_movies = similar_users_df_ordered.head(items)\n",
    "    \n",
    "#     # Lookup these movies in the main dataframe containing movie info to get their information\n",
    "#     movie_information = md[md['title'].isin(top_n_movies['title'])][['id', 'title', 'year', 'vote_average', 'genres']]\n",
    "    \n",
    "    return similar_users_df_ordered[:items]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b52ff1db",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>weighted_average</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>The Million Dollar Hotel</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>License to Wed</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Jarhead</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>All the Way Boys</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Monsieur  Hulot's Holiday</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>The Dark</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Say Anything...</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>The Science of Sleep</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Notting Hill</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Wag the Dog</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Contempt</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Stand by Me</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Ocean's Eleven</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Monsieur Ibrahim</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Dave Chappelle's Block Party</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            title  weighted_average\n",
       "116      The Million Dollar Hotel              0.73\n",
       "47                 License to Wed              0.37\n",
       "42                        Jarhead              0.27\n",
       "9                All the Way Boys              0.20\n",
       "60      Monsieur  Hulot's Holiday              0.09\n",
       "107                      The Dark              0.09\n",
       "86                Say Anything...              0.09\n",
       "122          The Science of Sleep              0.09\n",
       "70                   Notting Hill              0.09\n",
       "133                   Wag the Dog              0.09\n",
       "28                       Contempt              0.07\n",
       "95                    Stand by Me              0.07\n",
       "71                 Ocean's Eleven              0.07\n",
       "61               Monsieur Ibrahim              0.07\n",
       "30   Dave Chappelle's Block Party              0.07"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 15 movie recommendations for user with id 39 based on user based recommender system\n",
    "\n",
    "user_based_recommender(39, matrix_norm, 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "903f9e92",
   "metadata": {},
   "source": [
    "We can see that, depending upon the user that we enter, we get the relevant recommendations without even looking at the characteristics of the movie/users. It looks purely on how a user's ratings match up against other users to create a similarity profile and then selects the top rated movies (which our user hasn't watched) from those users (based on weighted average ratings) as recommendations. Let's now look at item based recommendations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b501d51",
   "metadata": {},
   "source": [
    "### <font color='green'><a id='the_destination_14'>4.1.2 Item-Based Collaborative Filtering</a></font>\n",
    "\n",
    "Item-based collaborative filtering algorithm usually has the following steps:\n",
    "\n",
    "1) Calculate item similarity scores based on all the user ratings\n",
    "2) Identify the top N items that are most similar to the item of interest\n",
    "3) Calculate the weighted average score for the most similar items by the user\n",
    "4) Rank items based on the score and pick top N items to recommend\n",
    "\n",
    "Since we are using the same data used in section 4.1.1, we can directly skip the intial steps and create the matrix. But this time the only difference is that we'll have movies as rows and users as columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "5d06957f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>48 Hrs.</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>5 Card Stud</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "<p>5 rows × 658 columns</p>\n",
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      ],
      "text/plain": [
       "userId                        2    3    4    5    6    7    8    9    10   \\\n",
       "title                                                                       \n",
       "20,000 Leagues Under the Sea  NaN  NaN  3.0  NaN  2.0  NaN  NaN  NaN  NaN   \n",
       "2001: A Space Odyssey         3.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "48 Hrs.                       5.0  NaN  NaN  4.0  NaN  NaN  NaN  NaN  NaN   \n",
       "5 Card Stud                   NaN  NaN  NaN  NaN  NaN  NaN  3.5  NaN  NaN   \n",
       "A Clockwork Orange            3.0  NaN  3.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "\n",
       "userId                        11   ...  662  663  664  665  666  667  668  \\\n",
       "title                              ...                                      \n",
       "20,000 Leagues Under the Sea  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2001: A Space Odyssey         NaN  ...  NaN  NaN  NaN  3.0  NaN  NaN  NaN   \n",
       "48 Hrs.                       NaN  ...  4.0  NaN  NaN  NaN  4.0  4.0  NaN   \n",
       "5 Card Stud                   NaN  ...  NaN  NaN  4.0  5.0  NaN  NaN  NaN   \n",
       "A Clockwork Orange            NaN  ...  4.0  NaN  NaN  NaN  3.0  3.0  NaN   \n",
       "\n",
       "userId                        669  670  671  \n",
       "title                                        \n",
       "20,000 Leagues Under the Sea  NaN  NaN  NaN  \n",
       "2001: A Space Odyssey         NaN  NaN  NaN  \n",
       "48 Hrs.                       NaN  3.0  NaN  \n",
       "5 Card Stud                   NaN  NaN  5.0  \n",
       "A Clockwork Orange            NaN  NaN  NaN  \n",
       "\n",
       "[5 rows x 658 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_2 = user_data_final.pivot_table(index='title', columns='userId', values='rating')\n",
    "matrix_2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6862c1c",
   "metadata": {},
   "source": [
    "We'll do the same steps we did earlier to get the cosine similarity values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "016ed069",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 Card Stud</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A Clockwork Orange</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 658 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId                         2    3     4    5     6    7     8    9    10   \\\n",
       "title                                                                           \n",
       "20,000 Leagues Under the Sea  0.00  0.0  0.24  0.0 -0.76  0.0  0.00  0.0  0.0   \n",
       "2001: A Space Odyssey        -0.69  0.0  0.00  0.0  0.00  0.0  0.00  0.0  0.0   \n",
       "48 Hrs.                       1.10  0.0  0.00  0.1  0.00  0.0  0.00  0.0  0.0   \n",
       "5 Card Stud                   0.00  0.0  0.00  0.0  0.00  0.0 -0.68  0.0  0.0   \n",
       "A Clockwork Orange           -0.10  0.0 -0.10  0.0  0.00  0.0  0.00  0.0  0.0   \n",
       "\n",
       "userId                        11   ...  662  663   664   665  666  667  668  \\\n",
       "title                              ...                                        \n",
       "20,000 Leagues Under the Sea  0.0  ...  0.0  0.0  0.00  0.00  0.0  0.0  0.0   \n",
       "2001: A Space Odyssey         0.0  ...  0.0  0.0  0.00 -0.69  0.0  0.0  0.0   \n",
       "48 Hrs.                       0.0  ...  0.1  0.0  0.00  0.00  0.1  0.1  0.0   \n",
       "5 Card Stud                   0.0  ...  0.0  0.0 -0.18  0.82  0.0  0.0  0.0   \n",
       "A Clockwork Orange            0.0  ...  0.9  0.0  0.00  0.00 -0.1 -0.1  0.0   \n",
       "\n",
       "userId                        669  670   671  \n",
       "title                                         \n",
       "20,000 Leagues Under the Sea  0.0  0.0  0.00  \n",
       "2001: A Space Odyssey         0.0  0.0  0.00  \n",
       "48 Hrs.                       0.0 -0.9  0.00  \n",
       "5 Card Stud                   0.0  0.0  0.82  \n",
       "A Clockwork Orange            0.0  0.0  0.00  \n",
       "\n",
       "[5 rows x 658 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_norm_2 = round(matrix_2.subtract(matrix_2.mean(axis=1), axis = 'rows'),2)\n",
    "matrix_norm_2 = matrix_norm_2.fillna(0)\n",
    "matrix_norm_2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d47bef9",
   "metadata": {},
   "source": [
    "Let's write a function to find the top N similar items (movies) to a given item(movie) using cosine similarity. Here we're gonna use the users ratings for different movies to find the similarity between different movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "052d8ed7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def similarity_score(title, matrix):\n",
    "    \n",
    "    # create a df of just the given movie\n",
    "    item = matrix[matrix.index == title]\n",
    "    \n",
    "    # and a df of all other movies\n",
    "    other_items = matrix[matrix.index != title]\n",
    "    \n",
    "    # get cosine similarity between given movie and each of the other movies\n",
    "    other_items['similarity_score'] = cosine_similarity(item, other_items)[0].tolist()\n",
    "    \n",
    "    other_items_df = pd.DataFrame(other_items.to_records())\n",
    "    \n",
    "    return other_items_df.sort_values(by = 'similarity_score', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1a8fd04c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>663</th>\n",
       "      <th>664</th>\n",
       "      <th>665</th>\n",
       "      <th>666</th>\n",
       "      <th>667</th>\n",
       "      <th>668</th>\n",
       "      <th>669</th>\n",
       "      <th>670</th>\n",
       "      <th>671</th>\n",
       "      <th>similarity_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Silent Light</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.486193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Die Hard 2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.259884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Beauty and the Beast</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.239339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>The Great Outdoors</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.234471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Will Penny</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.224949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Men in Black II</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.74</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.080024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Madagascar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.093305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Superstar: The Karen Carpenter Story</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.22</td>\n",
       "      <td>-0.104490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>The Chronicles of Riddick: Dark Fury</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.106859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Sleepless in Seattle</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.51</td>\n",
       "      <td>-1.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.109539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>138 rows × 660 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    title    2    3     4     5    6    7  \\\n",
       "90                           Silent Light  0.0  0.0  0.00  0.00  0.0  0.0   \n",
       "32                             Die Hard 2  0.0  0.0  0.00  0.00  0.0  0.0   \n",
       "19                   Beauty and the Beast  0.0  0.0  0.00  0.00  0.0  0.0   \n",
       "112                    The Great Outdoors  0.0  0.0  0.00  0.00  0.0  0.0   \n",
       "135                            Will Penny  0.0  0.0  0.00  0.00  0.0  0.0   \n",
       "..                                    ...  ...  ...   ...   ...  ...  ...   \n",
       "58                        Men in Black II  0.0  0.0  0.00  0.00  0.0  0.0   \n",
       "57                             Madagascar  0.0  0.0  0.00  0.00  0.0  0.0   \n",
       "97   Superstar: The Karen Carpenter Story  0.0  0.0  1.22  0.00  0.0  0.0   \n",
       "104  The Chronicles of Riddick: Dark Fury  0.0  0.0  1.18  0.00  0.0  0.0   \n",
       "92                   Sleepless in Seattle  0.0  0.0  0.51 -1.99  0.0  0.0   \n",
       "\n",
       "        8     9   10  ...  663   664   665  666   667   668  669   670   671  \\\n",
       "90   0.00  0.00  0.0  ...  0.0  0.00 -0.27  0.0  0.00  0.00  0.0  0.00  0.00   \n",
       "32   0.00  0.00  0.0  ...  0.0  0.55  0.00  0.0  0.00  0.00  0.0  0.00  0.00   \n",
       "19   0.00  0.00  0.0  ...  0.0  0.97  0.00  0.0  0.00  0.00  0.0  0.00  0.00   \n",
       "112  0.00  0.00  0.0  ...  0.0  0.22  0.72  0.0  0.00  0.00  0.0  0.00  0.00   \n",
       "135  0.00  0.00  0.0  ...  0.0  0.90  0.00  0.0  0.00  0.00  0.0  0.00 -0.10   \n",
       "..    ...   ...  ...  ...  ...   ...   ...  ...   ...   ...  ...   ...   ...   \n",
       "58   0.00  0.74  0.0  ...  0.0  0.00  0.00  0.0  0.74  0.74  0.0  0.74 -0.26   \n",
       "57   0.00  0.00  0.0  ...  0.0  0.00  0.00  0.0  0.00  0.00  0.0  0.00  0.00   \n",
       "97   0.22  0.00  0.0  ...  0.0  0.00  0.00  0.0  0.00  0.00  0.0  0.00  0.22   \n",
       "104  0.68  0.00  0.0  ...  0.0  0.00  0.18  0.0  0.00  0.00  0.0  0.00  0.00   \n",
       "92   0.51  0.00  0.0  ...  0.0 -0.49  0.00  0.0  0.00  0.00  0.0  0.00  0.00   \n",
       "\n",
       "     similarity_score  \n",
       "90           0.486193  \n",
       "32           0.259884  \n",
       "19           0.239339  \n",
       "112          0.234471  \n",
       "135          0.224949  \n",
       "..                ...  \n",
       "58          -0.080024  \n",
       "57          -0.093305  \n",
       "97          -0.104490  \n",
       "104         -0.106859  \n",
       "92          -0.109539  \n",
       "\n",
       "[138 rows x 660 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie = 'Persepolis'\n",
    "similarity_score(movie, matrix_norm_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "043930e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def item_based_recommender(user, matrix, items = 5):\n",
    "    \n",
    "    # Getting the titles of movies the user has watched\n",
    "    user_watched_movies_df = pd.DataFrame(matrix[(matrix[user] != 0)][user])\n",
    "    user_watched_movies_df = pd.DataFrame(user_watched_movies_df.to_records())\n",
    "    user_watched_movies_df_title = user_watched_movies_df['title'].tolist()\n",
    "    \n",
    "    # Getting the titles of movies the user hasn't watched\n",
    "    user_unwatched_movies_df = pd.DataFrame(matrix[(matrix[user] == 0)][user])\n",
    "    user_unwatched_movies_df = pd.DataFrame(user_unwatched_movies_df.to_records())\n",
    "    user_unwatched_movies_title = user_unwatched_movies_df['title'].tolist()\n",
    "    \n",
    "    recommendations = {}\n",
    "    \n",
    "    # For each unwatched movie, getting the similarity score against the movies the user has watched\n",
    "    # Then calculating the weighted average to get the predicted rating\n",
    "    for movie in user_unwatched_movies_title:\n",
    "        sim_matrix = similarity_score(movie, matrix)\n",
    "        sim_matrix = sim_matrix[sim_matrix['title'].isin(user_watched_movies_df_title)][['title', str(user), 'similarity_score']]\n",
    "        weighted_rating = round(np.average(sim_matrix[str(user)], axis = 0, weights = sim_matrix['similarity_score']),2)\n",
    "        recommendations[movie] = weighted_rating\n",
    "    \n",
    "    # Convert to dataframe so its easy to sort and filter and then sorting by the predicted rating\n",
    "    recommendations_df = pd.DataFrame(recommendations.items(), columns = ['title', 'predicted_rating'])\n",
    "    recommendations_df = recommendations_df.sort_values(by = 'predicted_rating', ascending = False)\n",
    "    \n",
    "    return recommendations_df[:items]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "3e79cc03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>predicted_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Jarhead</td>\n",
       "      <td>1.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>The Prisoner of Zenda</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Silent Light</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Cheerleaders' Wild Weekend</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Popular Music</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Metropolis</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Dave Chappelle's Block Party</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Will Penny</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Persepolis</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Clockwork Orange</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>M</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Summer in Berlin</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Who Killed Bambi?</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Cockles and Muscles</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Avalon</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            title  predicted_rating\n",
       "34                        Jarhead              1.97\n",
       "99          The Prisoner of Zenda              0.96\n",
       "77                   Silent Light              0.72\n",
       "20     Cheerleaders' Wild Weekend              0.65\n",
       "64                  Popular Music              0.64\n",
       "48                     Metropolis              0.62\n",
       "24   Dave Chappelle's Block Party              0.58\n",
       "112                    Will Penny              0.57\n",
       "62                     Persepolis              0.55\n",
       "3              A Clockwork Orange              0.54\n",
       "45                              M              0.54\n",
       "81               Summer in Berlin              0.52\n",
       "111             Who Killed Bambi?              0.51\n",
       "21            Cockles and Muscles              0.51\n",
       "12                         Avalon              0.51"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 15 movie recommendations for user with id 39 based on item based recommender system\n",
    "\n",
    "item_based_recommender(39, matrix_norm_2, 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddefd363",
   "metadata": {},
   "source": [
    "## <font color='green'><a id='the_destination_15'>4.2 Model-Based Systems</a></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "228c17d1",
   "metadata": {},
   "source": [
    "To perform model-based recommendations, we will be leveraging the Surprise package (https://surpriselib.com/) which is a Python scikit for building and analyzing recommender systems that deal with explicit rating data. Surprise was designed with the following purposes in mind:\n",
    "\n",
    "- Give users perfect control over their experiments. To this end, a strong emphasis is laid on documentation, which we have tried to make as clear and precise as possible by pointing out every detail of the algorithms.\n",
    "- Alleviate the pain of Dataset handling. Users can use both built-in datasets (Movielens, Jester), and their own custom datasets.\n",
    "- Provide various ready-to-use prediction algorithms such as baseline algorithms, neighborhood methods, matrix factorization-based (SVD, PMF, SVD++, NMF), and many others. Also, various similarity measures (cosine, MSD, pearson…) are built-in.\n",
    "- Make it easy to implement new algorithm ideas.\n",
    "- Provide tools to evaluate, analyse and compare the algorithms’ performance. Cross-validation procedures can be run very easily using powerful CV iterators (inspired by scikit-learn excellent tools), as well as exhaustive search over a set of parameters.\n",
    "\n",
    "Firstly, using the Surprise library, we will benchmark all the algorithms present to see how they perform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "25f11121",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17629, 8)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will leverage the final user dataset we created after filtering out those movies with very few ratings\n",
    "\n",
    "user_data_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3029e457",
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = Reader(rating_scale=(0.5, 5))\n",
    "data = Dataset.load_from_df(user_data_final[['userId', 'movieId', 'rating']], reader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4175acba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating biases using als...\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Estimating biases using als...\n",
      "Estimating biases using als...\n",
      "Estimating biases using als...\n",
      "Estimating biases using als...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_rmse</th>\n",
       "      <th>test_mae</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>test_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SVDpp</th>\n",
       "      <td>0.847827</td>\n",
       "      <td>0.647816</td>\n",
       "      <td>0.890257</td>\n",
       "      <td>0.188886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNNBaseline</th>\n",
       "      <td>0.851323</td>\n",
       "      <td>0.651665</td>\n",
       "      <td>0.101074</td>\n",
       "      <td>0.696204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BaselineOnly</th>\n",
       "      <td>0.862887</td>\n",
       "      <td>0.662842</td>\n",
       "      <td>0.015186</td>\n",
       "      <td>0.014403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVD</th>\n",
       "      <td>0.862914</td>\n",
       "      <td>0.661981</td>\n",
       "      <td>0.167263</td>\n",
       "      <td>0.026862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SlopeOne</th>\n",
       "      <td>0.869431</td>\n",
       "      <td>0.660657</td>\n",
       "      <td>0.027207</td>\n",
       "      <td>0.106944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNNWithZScore</th>\n",
       "      <td>0.869912</td>\n",
       "      <td>0.665087</td>\n",
       "      <td>0.127898</td>\n",
       "      <td>0.627372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNNWithMeans</th>\n",
       "      <td>0.874020</td>\n",
       "      <td>0.670770</td>\n",
       "      <td>0.100652</td>\n",
       "      <td>0.621841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNNBasic</th>\n",
       "      <td>0.885721</td>\n",
       "      <td>0.679806</td>\n",
       "      <td>0.084055</td>\n",
       "      <td>0.573327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NMF</th>\n",
       "      <td>0.899494</td>\n",
       "      <td>0.689925</td>\n",
       "      <td>0.268602</td>\n",
       "      <td>0.024808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CoClustering</th>\n",
       "      <td>0.906102</td>\n",
       "      <td>0.701993</td>\n",
       "      <td>0.366265</td>\n",
       "      <td>0.040444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NormalPredictor</th>\n",
       "      <td>1.349853</td>\n",
       "      <td>1.073245</td>\n",
       "      <td>0.020796</td>\n",
       "      <td>0.025601</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 test_rmse  test_mae  fit_time  test_time\n",
       "Algorithm                                                \n",
       "SVDpp             0.847827  0.647816  0.890257   0.188886\n",
       "KNNBaseline       0.851323  0.651665  0.101074   0.696204\n",
       "BaselineOnly      0.862887  0.662842  0.015186   0.014403\n",
       "SVD               0.862914  0.661981  0.167263   0.026862\n",
       "SlopeOne          0.869431  0.660657  0.027207   0.106944\n",
       "KNNWithZScore     0.869912  0.665087  0.127898   0.627372\n",
       "KNNWithMeans      0.874020  0.670770  0.100652   0.621841\n",
       "KNNBasic          0.885721  0.679806  0.084055   0.573327\n",
       "NMF               0.899494  0.689925  0.268602   0.024808\n",
       "CoClustering      0.906102  0.701993  0.366265   0.040444\n",
       "NormalPredictor   1.349853  1.073245  0.020796   0.025601"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Iterating over all algorithms\n",
    "\n",
    "from surprise import SVD, SVDpp, SlopeOne, NMF, NormalPredictor, KNNBaseline, KNNBasic, KNNWithMeans, KNNWithZScore, BaselineOnly, CoClustering\n",
    "\n",
    "benchmark = []\n",
    "\n",
    "for algorithm in [SVD(), SVDpp(), SlopeOne(), NMF(), NormalPredictor(), KNNBaseline(), KNNBasic(), KNNWithMeans(), KNNWithZScore(), BaselineOnly(), CoClustering()]:\n",
    "    # Perform cross validation\n",
    "    results = cross_validate(algorithm, data, measures=['RMSE', 'MAE'], cv=5, verbose=False)\n",
    "    \n",
    "    # Get results & append algorithm name\n",
    "    tmp = pd.DataFrame.from_dict(results).mean(axis=0)\n",
    "    tmp = tmp.append(pd.Series([str(algorithm).split(' ')[0].split('.')[-1]], index=['Algorithm']))\n",
    "    benchmark.append(tmp)\n",
    "    \n",
    "pd.DataFrame(benchmark).set_index('Algorithm').sort_values('test_rmse')    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38eda9e9",
   "metadata": {},
   "source": [
    "SVDpp algorithm gives us the best rmse, therefore, we will proceed further with SVDpp.  \n",
    "\n",
    "We will first train the SVDpp algorithm on our movies data. Then we'll move ahead and predict ratings for all user-item pairs (u, i) that are NOT in the training set. Finally we will create a function that will give N recommendations for a given user using the model we trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "09373b25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " processing epoch 0\n",
      " processing epoch 1\n",
      " processing epoch 2\n",
      " processing epoch 3\n",
      " processing epoch 4\n",
      " processing epoch 5\n",
      " processing epoch 6\n",
      " processing epoch 7\n",
      " processing epoch 8\n",
      " processing epoch 9\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "# Getting the top N recommendations for all users\n",
    "\n",
    "def get_top_n(predictions, N):\n",
    "    \"\"\"Return the top-N recommendation for each user from a set of predictions.\n",
    "    Args:\n",
    "        predictions(list of Prediction objects): The list of predictions, as\n",
    "            returned by the test method of an algorithm.\n",
    "        N(int): The number of recommendation to output for each user\n",
    "    Returns:\n",
    "    A dict where keys are user (raw) ids and values are lists of tuples:\n",
    "        [(raw item id, rating estimation), ...] of size n.\n",
    "    \"\"\"\n",
    "\n",
    "    # First map the predictions to each user.\n",
    "    top_n = defaultdict(list)\n",
    "    for uid, iid, true_r, est, _ in predictions:\n",
    "        top_n[uid].append((iid, est))\n",
    "\n",
    "    # Then sort the predictions for each user and retrieve the k highest ones.\n",
    "    for uid, user_ratings in top_n.items():\n",
    "        user_ratings.sort(key=lambda x: x[1], reverse=True)\n",
    "        top_n[uid] = user_ratings[:N]\n",
    "\n",
    "    return top_n\n",
    "\n",
    "# Creating a function to show the top N recommendations for a given user\n",
    "\n",
    "def model_based_recommender(user, N = 10):\n",
    "    \n",
    "    top_n = get_top_n(predictions, N)\n",
    "    user_top_n = top_n[user]\n",
    "    user_top_n_df = pd.DataFrame(user_top_n, columns = ['movieId', 'predicted_rating'])\n",
    "    user_top_n_df = user_top_n_df.merge(md[['title', 'id']], how = 'inner', left_on = 'movieId', right_on = 'id')\n",
    "    user_top_n_df['predicted_rating'] = round(user_top_n_df['predicted_rating'], 2)\n",
    "    del user_top_n_df['id']\n",
    "    return user_top_n_df[['movieId', 'title', 'predicted_rating']]\n",
    "\n",
    "\n",
    "# First train the SVDpp algorithm on our entire movies data\n",
    "trainset = data.build_full_trainset()\n",
    "algo = SVDpp(verbose=True, n_epochs=10)\n",
    "algo.fit(trainset)\n",
    "\n",
    "# Then predict ratings for all pairs (u, i) that are NOT in the training set \n",
    "# (basically those user-item pairs where the user has not interacted with the item in the past)\n",
    "testset = trainset.build_anti_testset()\n",
    "predictions = algo.test(testset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "1671c99d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>predicted_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>318</td>\n",
       "      <td>The Million Dollar Hotel</td>\n",
       "      <td>4.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>858</td>\n",
       "      <td>Sleepless in Seattle</td>\n",
       "      <td>4.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>527</td>\n",
       "      <td>Once Were Warriors</td>\n",
       "      <td>4.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>913</td>\n",
       "      <td>The Thomas Crown Affair</td>\n",
       "      <td>4.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58559</td>\n",
       "      <td>Confession of a Child of the Century</td>\n",
       "      <td>4.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>778</td>\n",
       "      <td>Monsieur  Hulot's Holiday</td>\n",
       "      <td>4.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4993</td>\n",
       "      <td>5 Card Stud</td>\n",
       "      <td>4.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4226</td>\n",
       "      <td>Shriek If You Know What I Did Last Friday the ...</td>\n",
       "      <td>4.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1252</td>\n",
       "      <td>Lonely Hearts</td>\n",
       "      <td>4.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4973</td>\n",
       "      <td>Under the Sand</td>\n",
       "      <td>4.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>750</td>\n",
       "      <td>Murder She Said</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2959</td>\n",
       "      <td>License to Wed</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>260</td>\n",
       "      <td>The 39 Steps</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2324</td>\n",
       "      <td>Local Color</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>912</td>\n",
       "      <td>The Thomas Crown Affair</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movieId                                              title  \\\n",
       "0       318                           The Million Dollar Hotel   \n",
       "1       858                               Sleepless in Seattle   \n",
       "2       527                                 Once Were Warriors   \n",
       "3       913                            The Thomas Crown Affair   \n",
       "4     58559               Confession of a Child of the Century   \n",
       "5       778                          Monsieur  Hulot's Holiday   \n",
       "6      4993                                        5 Card Stud   \n",
       "7      4226  Shriek If You Know What I Did Last Friday the ...   \n",
       "8      1252                                      Lonely Hearts   \n",
       "9      4973                                     Under the Sand   \n",
       "10      750                                    Murder She Said   \n",
       "11     2959                                     License to Wed   \n",
       "12      260                                       The 39 Steps   \n",
       "13     2324                                        Local Color   \n",
       "14      912                            The Thomas Crown Affair   \n",
       "\n",
       "    predicted_rating  \n",
       "0               4.72  \n",
       "1               4.67  \n",
       "2               4.53  \n",
       "3               4.52  \n",
       "4               4.48  \n",
       "5               4.44  \n",
       "6               4.44  \n",
       "7               4.42  \n",
       "8               4.41  \n",
       "9               4.40  \n",
       "10              4.39  \n",
       "11              4.38  \n",
       "12              4.38  \n",
       "13              4.38  \n",
       "14              4.38  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 15 movie recommendations for user with id 39 based on model based recommender system\n",
    "\n",
    "model_based_recommender(39,15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93969c91",
   "metadata": {},
   "source": [
    "Hopefully this gives you a brief understanding of how both memory-based and model-based Collaborative filtering systems work. Let's take a look at the pros and cons of Collaborative Filtering:\n",
    "\n",
    "<b>Pros:</b>\n",
    "\n",
    "- <b>No Features</b>: Doesn't require the features of other users/movies except ratings\n",
    "- <b>Simple</b>: Simplicity of implementation\n",
    "- <b>Explainable</b>: Good explainability of the results\n",
    "- <b>Easy</b>: Easiness of adding new users\n",
    "\n",
    "<b>Cons:</b>\n",
    "\n",
    "- <b>Cold Start</b> : New users will have little to no information about them to be compared with other users\n",
    "- <b>Sparsity</b>: The percentage of people who rate items is generally low\n",
    "- <b>Scalability</b>: The more K neighbors we consider, the better the classification. But, the more users there are, greater the cost of finding the K nearest neighbors\n",
    "- <b>New item</b>: New items will lack enough ratings to be considered for a meaningful recommendation\n",
    "    \n",
    "This concludes our overview on recommendation systems. Hopefully I was able to explain how they work and was able to guide you towards other resources for more learning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a138fc04",
   "metadata": {},
   "source": [
    "# <font color='green'>References</font>\n",
    "\n",
    "https://www.kaggle.com/code/rounakbanik/movie-recommender-systems \\\n",
    "https://medium.com/grabngoinfo/recommendation-system-user-based-collaborative-filtering-a2e76e3e15c4 \\\n",
    "http://infolab.stanford.edu/~ullman/mmds/ch9.pdf \\\n",
    "https://towardsdatascience.com/building-and-testing-recommender-systems-with-surprise-step-by-step-d4ba702ef80b \\\n",
    "https://github.com/NicolasHug/Surprise/tree/master/examples"
   ]
  }
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