/Book-Recommendation-System

By implementing a clustering algorithm and collaborative filtering techniques, the system recommends books based on users' shared tastes in book selection. Explore the code and discover how the system delivers personalized book recommendations tailored to individual preferences.

Book Recommender System

This repository contains a Book Recommender System that utilizes clustering algorithms and collaborative filtering techniques to recommend books based on users' preferences and similarities in book selection.

Description

The Book Recommender System aims to provide personalized book recommendations to users by analyzing their preferences and matching them with other users who have similar tastes in books. The system employs a combination of clustering algorithms and collaborative filtering to generate accurate and relevant recommendations.

Features

  • Clustering Algorithm: The system utilizes a clustering algorithm to group users based on their book preferences. This helps in identifying users with similar tastes and interests.

  • Collaborative Filtering: By applying collaborative filtering techniques, the recommender system identifies books liked by users with similar preferences and recommends those books to other users who share the same taste.

  • Personalized Recommendations: The system provides personalized recommendations to each user based on their individual preferences and the preferences of similar users.

Getting Started

To use the Book Recommender System, follow these steps:

  1. Clone this repository to your local machine using git clone azimAVI/Book-Recommendation-System

  2. Install the required dependencies listed in the requirements.txt file.

  3. Run the main application file Book_Recommender_System.ipynb to start the recommender system.

Resources

For more information on the project and to access the source code, please visit my GitHub profile.

Contributions

Contributions to this project are welcome. If you encounter any issues or have suggestions for improvements, feel free to open an issue or submit a pull request.

Acknowledgements

  • The clustering algorithm used in this project is based on scikit-learn, a popular machine learning library.
  • Collaborative filtering techniques were implemented using Surprise, a Python scikit for building and analyzing recommender systems.

Feel free to explore the project and provide feedback. Happy reading and book recommendations!

Contact

For any questions or inquiries, please reach out to me