Personalized Book Recommendation Engine with Emotional Understanding and Contextual Relevance Based on Hybrid Filtering - A Novel Method Providing Recommendation Explanation

This paper introduces a novel personalized book recommendation system that integrates emotional understanding and contextual relevance to enhance the accuracy and personalization of suggestions. The system employs a hybrid filtering method, combining content-based and collaborative filtering, to address the limitations of traditional recommendation systems. By incorporating Explainable AI (XAI), the system provides transparent justifications for each recommendation, enhancing user trust and engagement. Additionally, sentiment analysis and contextual factors, such as weather, time of day, and the user's emotional state, are considered to ensure that the recommended books align with the user's current circumstances. Our findings show that the integration of Explainable AI significantly improved user satisfaction and engagement. Testing with 100 participants resulted in a high level of satisfaction, with 55% reporting that they found the recommendations more trustworthy due to explanations. The system makes a significant contribution to improving emotional well-being by providing personalized recommendations.