A Book Tracking and Recommender System using Machine Learning Algorithms

Abstract: Book recommendation systems play a crucial role in enhancing user experience by suggesting books tailored to individual preferences. Traditional approaches, such as collaborative filtering and content-based filtering, have limitations, including cold-start issues and lack of diversity in recommendations. This paper proposes a hybrid book recommendation system that integrates content-based filtering with popularity-based filtering to generate personalized yet diverse book suggestions. The system utilizes vector similarityfor content-based recommendations while leveraging user engagement metrics to identify popular books. The proposed model is evaluated based on recommendation effectiveness, diversity, and user engagement. Results demonstrate that the hybrid approach improves personalization while mitigating the limitations of individual filtering methods. Future work includes integrating deep learning techniques and natural language processing (NLP) for further optimization