The way people find and read books has changed a lot because of all the content on the internet. There are many books in online stores, digital libraries, and educational websites. This has made it hard for people to find books that they really want to read. They have to look through many books that is hard to know what is good. People usually look for books by searching or considering what type of book it is. They also look at what's popular with everyone else. This does not really help them find books that they will like. To make it easier for people to find books, this research focuses on a new way to recommend books. This new way uses collaborative filtering to suggest books that people will really like. This recommendation system is designed to help people find books that are just right for them. The proposed system utilizes historical user–book interaction data, including user ratings and implicit behavioral patterns, to model user preferences and identify similarity relationships within the dataset. A structured user–item interaction matrix is constructed to represent the relationships between users and books. Both user-based and item-based collaborative filtering approaches are implemented to capture similarity patterns. Cosine similarity is employed as the primary similarity metric to measure the closeness between user preference vectors and item feature vectors, enabling the system to identify similar users or similar books effectively. Based on computed similarity scores, the system generates Top-N personalized recommendations tailored to individual user profiles.
Machine Learning, Collaborative Filtering, Personalized Recommendation, User–Item Interaction, Cosine Similarity, Recommender Systems.
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