This is a collaborative book recommender system that suggests similar books based on user's reading history and other users' preferences.
To run this recommendation system, you will need the following:
Python 3.9.12
Pandas
Numpy
Scikit-learn
The system uses 3 datasets: books, users and ratings. The datasets can be downloaded from the given link: https://www.kaggle.com/datasets/saurabhbagchi/books-dataset
The recommendation system is implemented in the brs.py file. The code performs the following steps:
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Cleaning dataset
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Selecting users and books with more than 100 books.
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Applying Pivot Table.
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Calculate the cosine similarity.
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Make recoomendations of similar books to the users based on their reading history
Clone this repository to your local machine:
git clone https://github.com/jhasiddhant/book-recommender-system.git
Install the required packages:
pip install -r requirements.txt
Run the brs.py file:
python brs.py
When prompted, enter the name of a book to receive recommendations for book based on users history who have read that book.
Enter book name: a painted house
Recommended books :
The Brethren
We Were The Mulvaneys
The Firm
The Pelican Brief
Cradle And All
The Chamber
The Testament
The Lovely Bones: A Novel
The King Of Torts
Fall On Your Knees