Book Recommendation System
A book recommendation system using collaborative filtering with a Streamlit frontend.
Features
- Collaborative filtering for personalized book recommendations.
- Interactive Streamlit web interface.
- Utilizes book ratings, book information, and user details for recommendations.
Dataset
- Books: Includes details like author, title, and publication year.
- Users: Contains user IDs and locations.
- Ratings: Ratings provided by users for various books.
Preprocessing
- Removed unnecessary columns.
- Renamed columns for ease of use.
- Filtered to include users with at least 200 ratings and books with at least 50 ratings.
Collaborative Filtering
Collaborative filtering is a method used to make automatic predictions about the interests of a user by collecting preferences or taste information from many users. The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B’s opinion on a different issue than that of a randomly chosen person.
In this project, collaborative filtering is used to find similarities between users based on their book ratings. Books are then recommended to a user based on the ratings from similar users. This technique helps in making personalized book recommendations even when the user has not rated many books themselves.
Model Building
- Created a pivot table with user IDs as columns, book titles as rows, and ratings as values.
- Converted the pivot table to a sparse matrix.
- Trained a Nearest Neighbors model using the sparse matrix.
Streamlit Interface
- Select a book from the dropdown menu.
- Click the “Show Recommendation” button to view recommended books and their images.
GitHub Repository
Explore the source code and contribute to the project on GitHub: View on GitHub
Conclusion
The system effectively recommends books based on user preferences and provides an intuitive interface for obtaining book suggestions.