Book Recommendation System

A book recommendation system built using Popularity-Based Recommender, Collaborative Filtering, and Cosine Similarity. This project provides personalized book recommendations to users based on their preferences. It is implemented using Flask as the web framework.

Watch the demo video:

Click here to watch the demo video

Features

• Popularity-Based Recommendation: Suggests books based on their popularity (e.g., top-rated books).
• Collaborative Filtering: Recommends books to a user based on the preferences of similar users.
• Cosine Similarity: Used to calculate similarity between user preferences and book attributes for recommendations.
• Flask Web App: A user-friendly interface where users can enter a book title and get recommendations.

Tech Stack:

• Python: Core language for building the recommendation system.
• Flask: Web framework for creating the book recommender application.
• Pandas, Numpy, Matplotlib, Seaborn: Data manipulation and Visualization for handling, understanding book and user data.
• Cosine Similarity: Measure of similarity between two vectors of user preferences.
• Bootstrap: Front-end framework for responsive design.

Usage:

1. Home Page: Users see a collection of top 50 books using popularty based filtering.
2. Recommendation Page: After entering a book title, users will be presented with a list of recommended books, sorted based on collaborative filtering.
3. Recommendation Types:
   • Popularity-Based: Recommends top books based on overall ratings and votes.
   • Collaborative Filtering: Uses user behavior (such as previous ratings and preferences) to recommend books.
   • Cosine Similarity: Recommends books by finding similarities between user ratings or book attributes.

Directory Structure:

Directory structure:
└── 04bhavyaa-book-recommendation-system/
    ├── book-recommendation-system.ipynb
    ├── app.py
    ├── book-data-eda.ipynb
    ├── data/
    │   ├── ratings_books_users.csv
    │   ├── book_data.pkl
    │   ├── popular_books.pkl
    │   ├── Ratings.csv
    │   ├── Users.csv
    │   ├── similarity_score.pkl
    │   ├── pivot_table_data.pkl
    │   └── Books.csv
    ├── README.md
    ├── templates/
    │   ├── index.html
    │   └── recommend.html
    └── static/
        └── styles.css

Future Enhancements

1. Personalization: Allow users to create an account, rate books, and provide more tailored recommendations.
2. Machine Learning Models: Use advanced machine learning models like matrix factorization or deep learning for better recommendations.
3. Integration with Book APIs: Integrate with external APIs (like Google Books or Open Library) to fetch real-time book data and improve recommendations.