Bookbuddy: A Mood Based Book Recommendation System

Nowadays, with the abundance of books available, readers often struggle to find books that align with their personal interests and moods. This paper proposes a personalized reading system that leverages machine learning techniques to provide book recommendations based on users' individual preferences and emotional states. The system integrates popularity-based, collaborative, and mood-based filtering approaches to generate highly personalized recommendations that cater to users' unique reading interests and mood. To facilitate user engagement and interaction, the system incorporates a database of books, reviews, and ratings, and allows users to access E-Books for free and purchase hard copies if desired. The paper outlines the system's design and implementation, highlighting its potential to improve users' reading experience and encourage exploration of new books and authors. The proposed system represents a significant contribution to the field of personalized recommendation systems and demonstrates the effectiveness of machine learning techniques in generating tailored recommendations.