<![CDATA[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.]]>
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