Collaborative Filtering Based Book Recommender System

Introduction

This is a collaborative book recommender system that suggests similar books based on user's reading history and other users' preferences.

Requirements

To run this recommendation system, you will need the following:

Python 3.9.12
Pandas
Numpy
Scikit-learn

Dataset

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

Implementation

The recommendation system is implemented in the brs.py file. The code performs the following steps:

1. Cleaning dataset

2. Selecting users and books with more than 100 books.

3. Applying Pivot Table.

4. Calculate the cosine similarity.

5. Make recoomendations of similar books to the users based on their reading history

Installation

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.

Example:

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