This is a collaborative filtering-based book recommendation system built with Flask. The project uses a dataset containing information about books, user ratings, and user profiles to recommend books to users based on their preferences.
- Popular Books: Displays the top 50 most popular books based on ratings and votes.
- Book Recommendations: Recommends similar books based on a user's input.
- Web Interface: A simple and clean web interface built with Bootstrap for easy interaction.
The project uses the following dataset files:
- Books: Contains information about different books including titles, authors, and image URLs.
- Ratings: Includes user ratings for various books.
- Users: Contains information about users.
The recommendation system uses collaborative filtering to find books that are similar to a user-selected book. The similarity between books is calculated using precomputed similarity scores stored in a similarity_scores.pkl file. The app provides recommendations based on these scores.
- The user inputs a book title.
- The system retrieves the precomputed similarity scores for that book.
- The system recommends the top 8 books with the highest similarity scores.
You can try the Book Recommendation System live at https://book-recommendation-system-ynnn.onrender.com.