Book Recommendation System
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Project Overview
An advanced book recommendation system that combines collaborative filtering and content-based approaches using the Book-Crossings dataset. The system provides personalized book recommendations by analyzing both user behavior patterns and book content features.
Key Features
- Interactive book discovery system
- Multiple recommendation approaches:
- Popular books ranking with weighted scores
- Similar books through collaborative filtering
- Content-based recommendations by author/publisher
- Book cover image display
- Detailed book information and statistics
- User-friendly search and filtering
Technical Stack
- Backend: Python
- Frontend: Streamlit
- ML Models:
- Cosine Similarity
- TF-IDF Vectorization
- IMDB Weighted Rating System
- Data Processing: Pandas, NumPy, Scikit-learn
- Image Handling: Pillow, Requests
- Data Source: Kaggle API
- Visualization: Streamlit native components