📚 Book Recommendation System

🚀 Overview

This project implements a Book Recommendation System using multiple recommendation techniques:

• Popularity-Based Recommendation

• Content-Based Filtering (CBF)

• Collaborative Filtering (CF)

• Hybrid Model (switching between CBF and CF)

The system suggests books to users based on different strategies, considering user preferences, book similarities, and overall popularity trends.

💡 Features

🔥 Popularity-Based Recommendation

• Recommends books based on their overall popularity (highest-rated books).

• Does not consider individual user preferences.

🧠 Content-Based Filtering (CBF)

• Suggests books similar to those a user has previously liked.

• Uses book metadata such as Book title and author to compute similarity.

• Active users: Users who have rated more than 100 books.

Note: For exercise, I have left a issue in the current implementation if you have understood it correctly try to find and resolve it 😊.

👥 Collaborative Filtering (CF)

• Provides recommendations based on user behavior and preferences.

• Uses ratings from other users to suggest books.

• Defines:

  • Active users: Rated more than 100 books.

  • Expert users: Rated more than 300 books.

⚡ Hybrid Model

• Combines CBF and CF to leverage both content and user interactions.

• Provides a balanced recommendation strategy.

📂 Dataset Link - https://www.kaggle.com/datasets/arashnic/book-recommendation-dataset

📹 Demo Video

demo.mp4

🛠️ Tech Stack

• Python, Pandas, NumPy

• Scikit-learn (TF-IDF, MinMaxScaler, cosine similarity)

• FastAPI (for deployment)

• Matplotlib, Seaborn (for data analysis)

• Hugging Face Spaces (for live API)

▶️ Installation & Usage

Ensure you have the following installed:

pip install pandas numpy scikit-learn seaborn matplotlib fastapi uvicorn

👨‍💻 API Usage

Deployed via FastAPI on Hugging Face Spaces. Swagger UI available at:

https://vidhan66-book-recommender.hf.space/docs

Supports:

• /recommend/cbf?user_id=...
• /recommend/cf?user_id=...&book_name=...
• /recommend/hybrid?user_id=...&book_name=...

🤝 Contributing

We welcome contributions to enhance this project! To contribute:

1. Fork the repository – Click the ‘Fork’ button on GitHub.

2. Create a new branch – Use a meaningful name like feature-branch.

3. Make your changes – Ensure your code follows best practices and is well-documented.

4. Commit your changes – Write clear and descriptive commit messages.

5. Push your changes – Push your branch to your forked repository.

6. Submit a pull request (PR) – Explain what changes you made and why.

📄 Guidelines for Contributions:

• Issue Reporting: If you find a bug or have a feature request, open an issue on GitHub.

• Testing: Ensure your code runs correctly and does not break existing functionality.

• Documentation: Provide comments and update relevant documentation if needed.

We appreciate all contributions that help improve this project!

📜 License

This project is licensed under the MIT License – feel free to use, modify, and distribute it. See the LICENSE file for details.

📌 Future Enhancements

• Implement Deep Learning-based recommendations.

• Improve hybrid model using dynamic weighting.

Developed by Vidhan Bansal. Feel free to contribute or reach out for collaborations!