Personalized Mood-Centric Book Recommendation Integrating Machine Learning with Content Based Filtering

Abstract

This paper introduces a novel personalized book recommendation system aimed at enhancing subjective well-being (SWB). Despite the vast array of books available on the internet, people often struggle to find literature that aligns with their current emotional state. The system dynamically detects users' emotional states and recommends books tailored to their mood. It utilizes a content-based filtering algorithm to suggest top-rated books in real-time based on the user's current emotional state. For users with low mood, uplifting and inspirational books are recommended, while a mix of happy and sad books is suggested for happy users to acknowledge the nuanced interplay of emotions. Neutral-themed books are proposed to users with a neutral mood for balance. The system employs an emotion classification algorithm for accurate mood detection, analyzing book summaries to extract emotional tones. Users' emotional states are evaluated using a Likert scale, adjusting recommendations accordingly. The methodology involves data collection, preprocessing, emotion classification, and mood meter operation. Results demonstrate the system's effectiveness in providing tailored book recommendations, enriching users' reading experiences.