{"id":25809250,"url":"https://github.com/04bhavyaa/book-recommendation-system","last_synced_at":"2026-04-17T19:32:30.835Z","repository":{"id":270129401,"uuid":"909419660","full_name":"04bhavyaa/book-recommendation-system","owner":"04bhavyaa","description":"The Book Recommendation System provides personalized book suggestions using Popularity-Based Recommender, Collaborative Filtering, and Cosine Similarity. 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This project provides personalized book recommendations to users based on their preferences. It is implemented using Flask as the web framework.\n\n### Watch the demo video:\n[Click here to watch the demo video](Recommender-System.mp4)\n\n### Features\n- Popularity-Based Recommendation: Suggests books based on their popularity (e.g., top-rated books).\n- Collaborative Filtering: Recommends books to a user based on the preferences of similar users.\n- Cosine Similarity: Used to calculate similarity between user preferences and book attributes for recommendations.\n- Flask Web App: A user-friendly interface where users can enter a book title and get recommendations.\n\n### Tech Stack:\n- Python: Core language for building the recommendation system.\n- Flask: Web framework for creating the book recommender application.\n- Pandas, Numpy, Matplotlib, Seaborn: Data manipulation and Visualization for handling, understanding book and user data.\n- Cosine Similarity: Measure of similarity between two vectors of user preferences.\n- Bootstrap: Front-end framework for responsive design.\n\n### Usage: \n1. Home Page: Users see a collection of top 50 books using popularty based filtering.\n2. Recommendation Page: After entering a book title, users will be presented with a list of recommended books, sorted based on collaborative filtering.\n3. Recommendation Types:\n    - Popularity-Based: Recommends top books based on overall ratings and votes.\n    - Collaborative Filtering: Uses user behavior (such as previous ratings and preferences) to recommend books.\n    - Cosine Similarity: Recommends books by finding similarities between user ratings or book attributes.\n\n### Directory Structure:\n```\nDirectory structure:\n└── 04bhavyaa-book-recommendation-system/\n    ├── book-recommendation-system.ipynb\n    ├── app.py\n    ├── book-data-eda.ipynb\n    ├── data/\n    │   ├── ratings_books_users.csv\n    │   ├── book_data.pkl\n    │   ├── popular_books.pkl\n    │   ├── Ratings.csv\n    │   ├── Users.csv\n    │   ├── similarity_score.pkl\n    │   ├── pivot_table_data.pkl\n    │   └── Books.csv\n    ├── README.md\n    ├── templates/\n    │   ├── index.html\n    │   └── recommend.html\n    └── static/\n        └── styles.css\n```\n\n### Future Enhancements\n1. Personalization: Allow users to create an account, rate books, and provide more tailored recommendations.\n2. Machine Learning Models: Use advanced machine learning models like matrix factorization or deep learning for better recommendations.\n3. Integration with Book APIs: Integrate with external APIs (like Google Books or Open Library) to fetch real-time book data and improve recommendations.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F04bhavyaa%2Fbook-recommendation-system","html_url":"https://awesome.ecosyste.ms/projects/github.com%2F04bhavyaa%2Fbook-recommendation-system","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F04bhavyaa%2Fbook-recommendation-system/lists"}