recommender-system-using-amazon-reviews.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Amazon_logo.svg/320px-Amazon_logo.svg.png\" alt=\"Amazon\" /></center>\n",
    "<br>\n",
    "<center><font size=7 color=orange><b>Implementing a recommendation engine with Amazon Electronics data</b></font></center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>userId</th>\n",
       "      <th>productId</th>\n",
       "      <th>Rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AKM1MP6P0OYPR</td>\n",
       "      <td>0132793040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1365811200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2CX7LUOHB2NDG</td>\n",
       "      <td>0321732944</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1341100800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2NWSAGRHCP8N5</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1367193600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2WNBOD3WNDNKT</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1374451200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1GI0U4ZRJA8WN</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1334707200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824477</th>\n",
       "      <td>A2YZI3C9MOHC0L</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1396569600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824478</th>\n",
       "      <td>A322MDK0M89RHN</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1313366400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824479</th>\n",
       "      <td>A1MH90R0ADMIK0</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1404172800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824480</th>\n",
       "      <td>A10M2KEFPEQDHN</td>\n",
       "      <td>BT008UKTMW</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1297555200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7824481</th>\n",
       "      <td>A2G81TMIOIDEQQ</td>\n",
       "      <td>BT008V9J9U</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1312675200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7824482 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 userId   productId  Rating   timestamp\n",
       "0         AKM1MP6P0OYPR  0132793040     5.0  1365811200\n",
       "1        A2CX7LUOHB2NDG  0321732944     5.0  1341100800\n",
       "2        A2NWSAGRHCP8N5  0439886341     1.0  1367193600\n",
       "3        A2WNBOD3WNDNKT  0439886341     3.0  1374451200\n",
       "4        A1GI0U4ZRJA8WN  0439886341     1.0  1334707200\n",
       "...                 ...         ...     ...         ...\n",
       "7824477  A2YZI3C9MOHC0L  BT008UKTMW     5.0  1396569600\n",
       "7824478  A322MDK0M89RHN  BT008UKTMW     5.0  1313366400\n",
       "7824479  A1MH90R0ADMIK0  BT008UKTMW     4.0  1404172800\n",
       "7824480  A10M2KEFPEQDHN  BT008UKTMW     4.0  1297555200\n",
       "7824481  A2G81TMIOIDEQQ  BT008V9J9U     5.0  1312675200\n",
       "\n",
       "[7824482 rows x 4 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from surprise import KNNWithMeans\n",
    "from surprise import Dataset\n",
    "from surprise import accuracy\n",
    "from surprise import Reader\n",
    "from surprise.model_selection import train_test_split\n",
    "import scipy.sparse\n",
    "from scipy.sparse import csr_matrix\n",
    "from scipy.sparse.linalg import svds\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "from tabulate import tabulate\n",
    "\n",
    "# Load the Dataset and Add headers\n",
    "electronics_data = pd.read_csv(\"data/ratings_Electronics.csv\", names = ['userId', 'productId','Rating','timestamp'])\n",
    "electronics_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing values across columns:\n",
      "userId       0\n",
      "productId    0\n",
      "Rating       0\n",
      "timestamp    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Number of missing values across columns:', electronics_data.isnull().sum(), sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for duplicate rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate rows: 0\n"
     ]
    }
   ],
   "source": [
    "print('Number of duplicate rows:', electronics_data.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping the TimeStamp Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "electronics_data.drop(['timestamp'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset has 7824482 rows and 3 columns.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Dataset has {electronics_data.shape[0]} rows and {electronics_data.shape[1]} columns.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7824482 entries, 0 to 7824481\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Dtype  \n",
      "---  ------     -----  \n",
      " 0   userId     object \n",
      " 1   productId  object \n",
      " 2   Rating     float64\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 179.1+ MB\n"
     ]
    }
   ],
   "source": [
    "electronics_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7824482.00\n",
       "mean           4.01\n",
       "std            1.38\n",
       "min            1.00\n",
       "25%            3.00\n",
       "50%            5.00\n",
       "75%            5.00\n",
       "max            5.00\n",
       "Name: Rating, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Minimum rating is: 1\n",
      "Maximum rating is: 5\n"
     ]
    }
   ],
   "source": [
    "display(electronics_data.describe()['Rating'].T.round(2))\n",
    "\n",
    "print('\\n\\nMinimum rating is: %d' %(electronics_data['Rating'].min()))\n",
    "print('Maximum rating is: %d' %(electronics_data['Rating'].max()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total number of ratings</th>\n",
       "      <td>7824482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total number of unique Users</th>\n",
       "      <td>4201696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total number of unique Products</th>\n",
       "      <td>476002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Count\n",
       "Total number of ratings          7824482\n",
       "Total number of unique Users     4201696\n",
       "Total number of unique Products   476002"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([electronics_data.shape[0], electronics_data['userId'].nunique(), electronics_data['productId'].nunique()],\n",
    "    index=[\"Total number of ratings\", \"Total number of unique Users\", \"Total number of unique Products\"], columns=['Count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Important!` Since the dataset has no item features I cannot use content based filtering. For completely new users I will recommend them top 10 or 20 trending items based on popularity. For current users since number of products (476K) is less than number of users(4.2MM), so item-item collaborative filtering would make sense."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ratings Per User"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId\n",
       "A5JLAU2ARJ0BO     520\n",
       "ADLVFFE4VBT8      501\n",
       "A3OXHLG6DIBRW8    498\n",
       "A6FIAB28IS79      431\n",
       "A680RUE1FDO8B     406\n",
       "A1ODOGXEYECQQ8    380\n",
       "A36K2N527TXXJN    314\n",
       "A2AY4YUOX2N1BQ    311\n",
       "AWPODHOB4GFWL     308\n",
       "A25C2M3QF9G7OQ    296\n",
       "Name: Rating, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_of_ratings_per_user = electronics_data.groupby(by='userId')['Rating'].count().sort_values(ascending=False)\n",
    "no_of_ratings_per_user.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4201696.00\n",
       "mean           1.86\n",
       "std            2.89\n",
       "min            1.00\n",
       "25%            1.00\n",
       "50%            1.00\n",
       "75%            2.00\n",
       "max          520.00\n",
       "Name: Rating, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_of_ratings_per_user.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inference:** Looks like most people are providing ratings for 1 or 2 items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of the total 4201696 people who provided ratings, 1540 are individuals who have rated more than 50 products.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Out of the total {len(no_of_ratings_per_user)} people who provided ratings, {sum(no_of_ratings_per_user >= 50)} are individuals who have rated more than 50 products.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ratings Per Product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "productId\n",
       "B0074BW614    18244\n",
       "B00DR0PDNE    16454\n",
       "B007WTAJTO    14172\n",
       "B0019EHU8G    12285\n",
       "B006GWO5WK    12226\n",
       "B003ELYQGG    11617\n",
       "B003ES5ZUU    10276\n",
       "B007R5YDYA     9907\n",
       "B00622AG6S     9823\n",
       "B0002L5R78     9487\n",
       "Name: Rating, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_of_ratings_per_product = electronics_data.groupby(by='productId')['Rating'].count().sort_values(ascending=False)\n",
    "no_of_ratings_per_product.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    476002.00\n",
       "mean         16.44\n",
       "std         112.70\n",
       "min           1.00\n",
       "25%           1.00\n",
       "50%           2.00\n",
       "75%           7.00\n",
       "max       18244.00\n",
       "Name: Rating, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_of_ratings_per_product.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inference:** About half of the products have received 1 or 2 ratings. Average rating per product is 16 but this average is inflated due to right skewness of rating per product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of the total 476002 products, only 26226 products have received more than 50 ratings.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Out of the total {len(no_of_ratings_per_product)} products, only {sum(no_of_ratings_per_product >= 50)} products have received more than 50 ratings.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution of the rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.countplot(data=electronics_data, x='Rating')\n",
    "plt.title(\"Distribution of the Rating\")\n",
    "plt.ylabel(\"Number of Ratings\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inference:** Looks like most of the people has given the rating of 5!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ratings per user Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quantiles = no_of_ratings_per_user.quantile(np.arange(0, 1.01, 0.01), interpolation='higher') \n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "\n",
    "# Plot the quantiles as a line plot\n",
    "quantiles.plot()\n",
    "\n",
    "# Add scatter points for quantiles at 0.05 intervals\n",
    "plt.scatter(x=quantiles.index[::5], y=quantiles.values[::5], c='orange', label=\"quantiles with 0.05 intervals\")\n",
    "\n",
    "# Add scatter points for quantiles at 0.25 intervals\n",
    "plt.scatter(x=quantiles.index[::25], y=quantiles.values[::25], c='m', label=\"quantiles with 0.25 intervals\")\n",
    "\n",
    "plt.title(\"Ratings per product Distribution\")\n",
    "plt.ylabel('Number of ratings given by a user')\n",
    "plt.xlabel('Value at the quantile')\n",
    "plt.legend(loc='best')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ratings per product Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quantiles = no_of_ratings_per_product.quantile(np.arange(0, 1.01, 0.01), interpolation='higher') \n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "\n",
    "# Plot the quantiles as a line plot\n",
    "quantiles.plot()\n",
    "\n",
    "# Add scatter points for quantiles at 0.05 intervals\n",
    "plt.scatter(x=quantiles.index[::5], y=quantiles.values[::5], c='orange', label=\"quantiles with 0.05 intervals\")\n",
    "\n",
    "# Add scatter points for quantiles at 0.25 intervals\n",
    "plt.scatter(x=quantiles.index[::25], y=quantiles.values[::25], c='m', label=\"quantiles with 0.25 intervals\")\n",
    "\n",
    "plt.title(\"Ratings per product Distribution\")\n",
    "plt.ylabel('Number of ratings received by a product')\n",
    "plt.xlabel('Value at the quantile')\n",
    "plt.legend(loc='best')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inference:** We can see that both `ratings per user` and `ratings per product` are heavily clustered at the top end of the quantile."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Popularity Based Recommendation\n",
    "\n",
    "Popularity based recommendation system works with the trend. It basically uses the items which are in trend right now. For example, if any product which is usually bought by every new user then there are chances that it may suggest that item to the user who just signed up.\n",
    "\n",
    "The problems with popularity based recommendation system is that the personalization is not available with this method i.e. even though you know the behaviour of the user you cannot recommend items accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>productId</th>\n",
       "      <th>total ratings</th>\n",
       "      <th>mean rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0594451647</td>\n",
       "      <td>1</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0594481813</td>\n",
       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0970407998</td>\n",
       "      <td>2</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0972683275</td>\n",
       "      <td>4</td>\n",
       "      <td>4.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1400501466</td>\n",
       "      <td>6</td>\n",
       "      <td>3.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48185</th>\n",
       "      <td>B00LED02VY</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48186</th>\n",
       "      <td>B00LGN7Y3G</td>\n",
       "      <td>1</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48187</th>\n",
       "      <td>B00LGQ6HL8</td>\n",
       "      <td>5</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48188</th>\n",
       "      <td>B00LI4ZZO8</td>\n",
       "      <td>2</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48189</th>\n",
       "      <td>B00LKG1MC8</td>\n",
       "      <td>1</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48190 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        productId  total ratings  mean rating\n",
       "0      0594451647              1     5.000000\n",
       "1      0594481813              1     3.000000\n",
       "2      0970407998              2     2.500000\n",
       "3      0972683275              4     4.750000\n",
       "4      1400501466              6     3.333333\n",
       "...           ...            ...          ...\n",
       "48185  B00LED02VY              1     4.000000\n",
       "48186  B00LGN7Y3G              1     5.000000\n",
       "48187  B00LGQ6HL8              5     5.000000\n",
       "48188  B00LI4ZZO8              2     4.500000\n",
       "48189  B00LKG1MC8              1     5.000000\n",
       "\n",
       "[48190 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Product Ratings Approach: We set a threshold to consider products with minimum 50 ratings\n",
    "# products_ratings_min50 = electronics_data.groupby(\"productId\").filter(lambda x: x['Rating'].count() >= 50)\n",
    "# popular_50 = pd.DataFrame({\n",
    "#     \"productId\" : products_ratings_min50['productId'].unique(),\n",
    "#     'total ratings': products_ratings_min50.groupby('productId')['Rating'].count(),\n",
    "#     'mean rating': products_ratings_min50.groupby('productId')['Rating'].mean(),\n",
    "# }).reset_index(drop=True)\n",
    "\n",
    "\n",
    "\n",
    "# User Ratings Approach: We set a threshold to consider users who have rated more than 50 times\n",
    "user_ratings_min50 = electronics_data.groupby(\"userId\").filter(lambda x: x['Rating'].count() >= 50)\n",
    "\n",
    "popular_50 = pd.DataFrame({\n",
    "    \"productId\" : user_ratings_min50['productId'].unique(),\n",
    "    'total ratings': user_ratings_min50.groupby('productId')['Rating'].count(),\n",
    "    'mean rating': user_ratings_min50.groupby('productId')['Rating'].mean(),\n",
    "}).reset_index(drop=True)\n",
    "\n",
    "popular_50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**\n",
    "<p>Active users are emphasized in collaborative filtering, where recommendations are based on user behavior and preferences. The idea is to identify users with a substantial number of ratings, as these users contribute more information to the collaborative filtering process and thereby build a robust model. Active users also provide more reliable signals for similarity between items for item-item collaborative filtering.</p>\n",
    "<p>Active products are more relevant in content-based filtering, where recommendations are made by analyzing the features of items and suggesting similar ones.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "sns.barplot(x='total ratings', y='productId', data=popular_50.sort_values(by='total ratings', ascending=False).head(10), palette='viridis_r')\n",
    "plt.title('Top 10 popular products')\n",
    "plt.xlabel('Total Ratings')\n",
    "plt.ylabel('Product ID')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution of ratings per product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[8, 5])\n",
    "ax = plt.gca()\n",
    "\n",
    "plt.plot(popular_50['total ratings'].sort_values(ascending=False).values)\n",
    "plt.title('Number of ratings per Product')\n",
    "plt.xlabel('Products')\n",
    "plt.ylabel('Number of ratings')\n",
    "ax.set_xticklabels([])   # Removes the x-axis tick labels\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution of mean ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "sns.histplot(popular_50['mean rating'], bins=50, kde=True, color='blue')\n",
    "plt.title('Histogram of Mean Ratings')\n",
    "plt.xlabel('Mean Rating')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inference:** Average ratings of products are clusters at of the points 1, 2, 3, 4, 5. Look like the active users have consistently given high ratings of 5 to the majority of products!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean Rating vs Rating count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "sns.jointplot(x='mean rating', y='total ratings', data=popular_50, alpha=0.4, color='blue')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Item-Based Collaborative Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Prediction(uid='A294BKN913UOGP', iid='B0002E3WKO', r_ui=5.0, est=5, details={'actual_k': 0, 'was_impossible': False}),\n",
       " Prediction(uid='ANTN61S4L7WG9', iid='B00HFAEBWG', r_ui=5.0, est=4.162691459046874, details={'actual_k': 5, 'was_impossible': False}),\n",
       " Prediction(uid='AROQO2VDODT7', iid='B001G9TYHE', r_ui=5.0, est=5, details={'actual_k': 0, 'was_impossible': False}),\n",
       " Prediction(uid='A2YYHSORXIHZCS', iid='B000AMLU1Q', r_ui=3.0, est=4.261789374524737, details={'was_impossible': True, 'reason': 'User and/or item is unknown.'}),\n",
       " Prediction(uid='AYNAH993VDECT', iid='B00CPIF4G0', r_ui=4.0, est=4.0, details={'actual_k': 0, 'was_impossible': False})]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from surprise import KNNWithMeans\n",
    "from surprise import Dataset\n",
    "from surprise import accuracy\n",
    "from surprise import Reader\n",
    "from surprise.model_selection import train_test_split\n",
    "\n",
    "# Reading the dataset\n",
    "reader = Reader(rating_scale=(1, 5))   # ratings are on a scale from 1 to 5\n",
    "data = Dataset.load_from_df(user_ratings_min50, reader)\n",
    "\n",
    "trainset, testset = train_test_split(data, test_size=0.3,random_state=10)\n",
    "\n",
    "# k-NN collaborative filtering algorithm with means\n",
    "algo = KNNWithMeans(k=5, sim_options={'name': 'pearson_baseline', 'user_based': False}) # for item-based filtering\n",
    "algo.fit(trainset)\n",
    "\n",
    "test_pred = algo.test(testset)\n",
    "test_pred[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**\n",
    "<br>\n",
    "The test_pred variable contains predictions generated by the k-NN collaborative filtering algorithm with means on the testing set. Each prediction in the list includes information about a specific user-item interaction, providing insights into how well the model performs in estimating user preferences. It contains:\n",
    "- uid: User ID\n",
    "- iid: Item ID\n",
    "- r_ui: The true rating\n",
    "- est: The estimated rating\n",
    "- details: A dictionary containing additional details about the prediction\n",
    "<br>\n",
    "\n",
    "Some predictions have the details field indicating 'User and/or item is unknown.' This suggests that the model encountered instances where it lacked information about users or items during testing. This means that during the prediction process, the collaborative filtering algorithm encountered either a user or an item (or both) that it did not have information about in the training set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.1397\n",
      "Item-based Model : Test Set ->  1.139729533124278\n"
     ]
    }
   ],
   "source": [
    "print(f\"Item-based Model : Test Set -> \", accuracy.rmse(test_pred, verbose=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieve IDs of customers and products present in trainset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Users: ['ABQWMANK4AR63', 'A6VPK7X53QNAQ', 'A1YVO5V1SO18DZ', 'A3DOPYDOS49I3T', 'AWNJAY0M5UI70']\n",
      "Actual Items: ['B00FQ6CJ0W', 'B001Q9EZS2', 'B003Y5H17I', 'B00180QHCG', 'B00511PS3C']\n"
     ]
    }
   ],
   "source": [
    "raw_user_ids = [trainset.to_raw_uid(inner_id) for inner_id in trainset.all_users()]\n",
    "raw_item_ids = [trainset.to_raw_iid(inner_id) for inner_id in trainset.all_items()]\n",
    "\n",
    "print(\"Actual Users:\", raw_user_ids[:5])\n",
    "print(\"Actual Items:\", raw_item_ids[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let `B00511PS3C` be the product purchased by our target customer!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒═════╤═══════════════════╕\n",
      "│   # │ Recommendations   │\n",
      "╞═════╪═══════════════════╡\n",
      "│   1 │ B003ES5ZUU        │\n",
      "├─────┼───────────────────┤\n",
      "│   2 │ B0055CAR7Y        │\n",
      "├─────┼───────────────────┤\n",
      "│   3 │ B004CLYEE6        │\n",
      "├─────┼───────────────────┤\n",
      "│   4 │ B0013FRNKG        │\n",
      "├─────┼───────────────────┤\n",
      "│   5 │ B0049YQVHE        │\n",
      "├─────┼───────────────────┤\n",
      "│   6 │ B0002L5R78        │\n",
      "├─────┼───────────────────┤\n",
      "│   7 │ B008TCUXLW        │\n",
      "├─────┼───────────────────┤\n",
      "│   8 │ B00E98O7GC        │\n",
      "├─────┼───────────────────┤\n",
      "│   9 │ B00A35WYBA        │\n",
      "├─────┼───────────────────┤\n",
      "│  10 │ B001196H3S        │\n",
      "╘═════╧═══════════════════╛\n"
     ]
    }
   ],
   "source": [
    "item_id = 'B00511PS3C'\n",
    "number_of_similar_items = 10\n",
    "\n",
    "# Get the integer index for the item ID\n",
    "item_index = algo.trainset.to_inner_iid(item_id)\n",
    "\n",
    "# Get recommendations for the specified item\n",
    "item_recommendation_indices = algo.get_neighbors(item_index, k=number_of_similar_items)\n",
    "\n",
    "item_recommendations = [algo.trainset.to_raw_iid(idx) for idx in item_recommendation_indices]\n",
    "\n",
    "# Print Result\n",
    "print(tabulate(enumerate(item_recommendations, start=1), headers=[\"#\", \"Recommendations\"], tablefmt=\"fancy_grid\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let `A1YVO5V1SO18DZ` be the target customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒═════╤════════════════════╕\n",
      "│   # │ Recommendations    │\n",
      "╞═════╪════════════════════╡\n",
      "│   1 │ B00FQ6CJ0W         │\n",
      "├─────┼────────────────────┤\n",
      "│   2 │ B001Q9EZS2         │\n",
      "├─────┼────────────────────┤\n",
      "│   3 │ B00180QHCG         │\n",
      "├─────┼────────────────────┤\n",
      "│   4 │ B00511PS3C         │\n",
      "├─────┼────────────────────┤\n",
      "│   5 │ B00007FHDM         │\n",
      "├─────┼────────────────────┤\n",
      "│   6 │ B001DVZXC0         │\n",
      "├─────┼────────────────────┤\n",
      "│   7 │ B00168BSEW         │\n",
      "├─────┼────────────────────┤\n",
      "│   8 │ B002VPE1XY         │\n",
      "├─────┼────────────────────┤\n",
      "│   9 │ B002DUD36S         │\n",
      "├─────┼────────────────────┤\n",
      "│  10 │ B006WAPM78         │\n",
      "╘═════╧════════════════════╛\n"
     ]
    }
   ],
   "source": [
    "user_id = 'A1YVO5V1SO18DZ'\n",
    "number_of_similar_items = 10\n",
    "\n",
    "# Get the integer index for the user ID\n",
    "user_index = algo.trainset.to_inner_uid(user_id)\n",
    "\n",
    "# Get recommendations for the specified user\n",
    "user_recommendations_indices = algo.get_neighbors(user_index, k=number_of_similar_items)\n",
    "\n",
    "user_recommendations = [algo.trainset.to_raw_iid(idx) for idx in user_recommendations_indices]\n",
    "\n",
    "# Print Result\n",
    "print(tabulate(enumerate(user_recommendations, start=1), headers=[\"#\", \"Recommendations \"], tablefmt=\"fancy_grid\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒═════╤═════════════════╕\n",
      "│   # │ Similar Users   │\n",
      "╞═════╪═════════════════╡\n",
      "│   1 │ ABQWMANK4AR63   │\n",
      "├─────┼─────────────────┤\n",
      "│   2 │ A6VPK7X53QNAQ   │\n",
      "├─────┼─────────────────┤\n",
      "│   3 │ A3DOPYDOS49I3T  │\n",
      "├─────┼─────────────────┤\n",
      "│   4 │ AWNJAY0M5UI70   │\n",
      "├─────┼─────────────────┤\n",
      "│   5 │ A13WOT3RSXKRD5  │\n",
      "├─────┼─────────────────┤\n",
      "│   6 │ A365PBEOWM7EI7  │\n",
      "├─────┼─────────────────┤\n",
      "│   7 │ AGVWTYW0ULXHT   │\n",
      "├─────┼─────────────────┤\n",
      "│   8 │ A3HQAPXAJV7PRX  │\n",
      "├─────┼─────────────────┤\n",
      "│   9 │ ADLVFFE4VBT8    │\n",
      "├─────┼─────────────────┤\n",
      "│  10 │ A3VONQL7YW8UPM  │\n",
      "╘═════╧═════════════════╛\n"
     ]
    }
   ],
   "source": [
    "similar_users = [algo.trainset.to_raw_uid(idx) for idx in user_recommendations_indices]\n",
    "print(tabulate(enumerate(similar_users, start=1), headers=[\"#\", \"Similar Users\"], tablefmt=\"fancy_grid\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Report on Item-based Collaborative Filtering.\n",
    "- **Algorithm Configuration:** KNNWithMeans, was configured with user_based=False which sets preference for item-based collaborative filtering.\n",
    "- **Item-Item Collaborative Filtering:** The first segment of the code exemplifies item-item collaborative filtering. A specific item, 'B00511PS3C', is used as a reference point to retrieve recommendations for 10 similar products.\n",
    "- **User-Based Exploration:** In the second section, there is a deviation from the item-centric approach. A specific user, identified by the user ID 'A1YVO5V1SO18DZ', is input into the algorithm to obtain recommendations. On retrieving <u>items</u> the algorithm retrieves items that are recommended for the specified user based on the similarity of items they have interacted with. The recommended items can include products similar to those the user has purchased or interacted with in the past. These items are not necessarily new or unrated by the user. On retrieving </u>user</u> these users are not the target customers for whom recommendations are being sought; instead, they are users whose interactions with items are deemed similar to the specified user.\n",
    "- **Surprise Library Nuances:** In the Surprise library, even if we explicitly configure collaborative filtering as item-based it allows to use user-centric operations like getting neighbors based on a user ID. This the library is designed to handle both item-item and user-item collaborative filtering seamlessly. This design choice can sometimes lead to confusion in terminology.\n",
    "- **Recommendation:** For optimal clarity and alignment with the intended exploration of item-item collaborative filtering, it is advised to adhere to using dedicated item IDs when seeking recommendations with item-based approach."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model-based collaborative filtering system\n",
    "\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 approach. They have large coverage, even when working with large sparse matrices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "...                    ...             ...  ...            ...            ...   \n",
       "B00LED02VY             0.0             0.0  ...            0.0            0.0   \n",
       "B00LGN7Y3G             0.0             0.0  ...            0.0            0.0   \n",
       "B00LGQ6HL8             0.0             0.0  ...            0.0            0.0   \n",
       "B00LI4ZZO8             0.0             0.0  ...            0.0            0.0   \n",
       "B00LKG1MC8             0.0             0.0  ...            0.0            0.0   \n",
       "\n",
       "userId      AZ515FFZ7I2P7  AZ8XSDMIX04VJ  AZAC8O310IK4E  AZBXKUH4AIW3X  \\\n",
       "productId                                                                \n",
       "0594451647            0.0            0.0            0.0            0.0   \n",
       "0594481813            0.0            0.0            0.0            0.0   \n",
       "0970407998            0.0            0.0            0.0            0.0   \n",
       "0972683275            0.0            0.0            0.0            0.0   \n",
       "1400501466            0.0            0.0            0.0            0.0   \n",
       "...                   ...            ...            ...            ...   \n",
       "B00LED02VY            0.0            0.0            0.0            0.0   \n",
       "B00LGN7Y3G            0.0            0.0            0.0            0.0   \n",
       "B00LGQ6HL8            0.0            0.0            0.0            0.0   \n",
       "B00LI4ZZO8            0.0            0.0            0.0            0.0   \n",
       "B00LKG1MC8            0.0            0.0            0.0            0.0   \n",
       "\n",
       "userId      AZCE11PSTCH1L  AZMY6E8B52L2T  AZNUHQSHZHSUE  AZOK5STV85FBJ  \n",
       "productId                                                               \n",
       "0594451647            0.0            0.0            0.0            0.0  \n",
       "0594481813            0.0            0.0            0.0            0.0  \n",
       "0970407998            0.0            0.0            0.0            0.0  \n",
       "0972683275            0.0            0.0            0.0            0.0  \n",
       "1400501466            0.0            0.0            0.0            0.0  \n",
       "...                   ...            ...            ...            ...  \n",
       "B00LED02VY            0.0            0.0            0.0            0.0  \n",
       "B00LGN7Y3G            0.0            0.0            0.0            0.0  \n",
       "B00LGQ6HL8            0.0            0.0            0.0            0.0  \n",
       "B00LI4ZZO8            0.0            0.0            0.0            0.0  \n",
       "B00LKG1MC8            0.0            0.0            0.0            0.0  \n",
       "\n",
       "[48190 rows x 1540 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utility_matrix = user_ratings_min50.pivot_table(values='Rating', index='productId', columns='userId', fill_value=0)\n",
    "utility_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A utility matrix is a tabular representation of user-item interactions, commonly used in collaborative filtering. In this matrix, rows correspond to items (e.g., products), columns represent users, and each cell contains a user's rating for a specific item. The matrix is often `sparse`, as not all users rate all items. The objective of collaborative filtering is to predict missing ratings by identifying patterns and preferences in the existing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================================================================================================\n",
      "Shape of utility matrix: (48190, 1540)\n",
      "Shape of decomposed matrix: (48190, 10)\n",
      "Shape of correlation matrix: (48190, 48190)\n",
      "====================================================================================================\n",
      "The product that user has bought: B00511PS3C\n",
      "====================================================================================================\n",
      "╒═════╤══════════════════╕\n",
      "│   # │ Recommendation   │\n",
      "╞═════╪══════════════════╡\n",
      "│   1 │ B003BLQHN4       │\n",
      "├─────┼──────────────────┤\n",
      "│   2 │ B00GP4BVTO       │\n",
      "├─────┼──────────────────┤\n",
      "│   3 │ B00CAFPF26       │\n",
      "├─────┼──────────────────┤\n",
      "│   4 │ B0082YEH8M       │\n",
      "├─────┼──────────────────┤\n",
      "│   5 │ B00141AZR2       │\n",
      "├─────┼──────────────────┤\n",
      "│   6 │ B007PRHNHO       │\n",
      "├─────┼──────────────────┤\n",
      "│   7 │ B00127OVHI       │\n",
      "├─────┼──────────────────┤\n",
      "│   8 │ B006PWGEQQ       │\n",
      "├─────┼──────────────────┤\n",
      "│   9 │ B0018MI5F6       │\n",
      "├─────┼──────────────────┤\n",
      "│  10 │ B004QK7HI8       │\n",
      "╘═════╧══════════════════╛\n",
      "====================================================================================================\n"
     ]
    }
   ],
   "source": [
    "print(\"=\"*100, f\"\\nShape of utility matrix: {utility_matrix.shape}\", sep='')\n",
    "\n",
    "# Decomposing the utility matrix using Truncated SVD with 10 components\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "SVD = TruncatedSVD(n_components=10)\n",
    "decomposed_matrix = SVD.fit_transform(utility_matrix)\n",
    "print(f\"Shape of decomposed matrix: {decomposed_matrix.shape}\")\n",
    "\n",
    "# Calculating pairwise correlations between all products\n",
    "correlation_matrix = np.corrcoef(decomposed_matrix)\n",
    "print(f\"Shape of correlation matrix: {correlation_matrix.shape}\\n\", \"=\"*100, sep='')\n",
    "\n",
    "# Let \"B00511PS3C\" be the product purchased by our target customer\n",
    "i = \"B00511PS3C\"\n",
    "print(f\"The product that user has bought: {i}\\n\", \"=\"*100, sep='')\n",
    "\n",
    "# List of available products\n",
    "product_names = list(utility_matrix.index)\n",
    "\n",
    "# Extracting row number of i in utility matrix\n",
    "product_location = product_names.index(i)\n",
    "\n",
    "# Correlation for all items with i based on items rated by other customers\n",
    "correlation = correlation_matrix[product_location]\n",
    "\n",
    "# Creating a DataFrame with product names and their correlation coefficients\n",
    "correlation_df = pd.DataFrame({'Product': product_names, 'Correlation': correlation})\n",
    "\n",
    "# Sorting the DataFrame by correlation in descending order\n",
    "sorted_recommendations = correlation_df.sort_values(by='Correlation', ascending=False)\n",
    "\n",
    "# Recommending top 25 highly correlated products\n",
    "top_recommendations = list(sorted_recommendations['Product'][1:11])  # excluding the item already bought\n",
    "\n",
    "table = tabulate(enumerate(top_recommendations, start=1), headers=[\"#\", \"Recommendation\"], tablefmt=\"fancy_grid\")\n",
    "print(table, \"\\n\", \"=\"*100, sep='')"
   ]
  }
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