Book Recommendation System Using Hybrid Content and Collaborative Filtering Techniques

In this study, the process of producing a book recommendation system outlines the process of finding books to provide personalized suggestions according to user preference and behavior. The system increases user satisfaction by tailoring recommendations to their tastes, improving the overall reading experience, and enhancing engagement and retention on plat-forms. It tracks users with collaborative filtering. interactions, content-based filtering to study book characteristics such as genre and author, and mixed methods to combine both techniques that will help ensure accuracy and circumvent similar limitations of cold start problems. Machine learning models-, pre-processing of data algorithms, and accuracy and recall measures of analysis ensure the effectiveness of the system in providing individualized and relevant recommendations that are 85% accurate. The findings reveal test the system's capacity to generate good suggestions, improving user experience and engagement. Improvements in the future could include real-time recommendations and broader feedback integration towards further optimization in accuracy and usability.