Intelligence Book Recommendation System Using Collaborative Filtering

Abstract

The rapid growth of online literary material has changed the way users discover books, revealing the limitations of traditional recommendation algorithms. This paper presents a review about an intelligent book recommendation system that uses collaborative filtering (CF) and artificial intelligence techniques to address major obstacles such as cold-start issues, data scarcity, and privacy concerns. The suggested method guarantees customized, accurate, and diversified recommendations by merging hybrid approaches such as CF with content-based filtering and matrix factorization. To measure performance, the researchers employ publicly accessible datasets, rigorous preprocessing approaches, and assessment criteria like as accuracy, recall, and F1-score. This project intends to rethink the book discovery process by solving basic issues and applying a privacy-conscious design, while also providing a scalable and user-friendly platform for tailored recommendations.