Build a Personalized Book Recommendation Engine

In the ocean of available books, users often struggle to find titles matching their taste. Manual browsing is time-consuming. A personalized recommendation engine analyzes user behavior and preferences to suggest books tailored to their unique interests, enhancing user experience and engagement.

Develop a machine learning-based recommendation system that uses collaborative filtering, content-based filtering, or hybrid techniques to suggest books. The system continuously learns from user feedback (likes, ratings, reads) to refine and personalize future recommendations.

Users browse or rate books. The system builds a profile based on their actions (genres liked, books read, ratings given). Using machine learning algorithms, it predicts new books they might enjoy. Over time, the more users interact, the better and more accurate the recommendations become.

• Users create a profile, select interests, and optionally rate some initial books.
• Recommendation engine applies collaborative filtering, content-based filtering, or hybrid approaches.
• Suggestions update dynamically as users interact (rate, read, like, dislike) with the system.
• Advanced filtering based on genres, authors, user moods (e.g., “feel-good”, “thriller”, “motivational”) available.
• Optional: Integration with external APIs (Goodreads, Google Books) for dynamic book metadata enrichment.

• Building a Recommendation System with Python
• Collaborative Filtering Explained (Medium)
• Using Content-Based Filtering in Recommendation Systems
• MongoDB Data Modeling for Recommender Systems
• Goodreads API Documentation
• Best Practices for Machine Learning in Recommenders