Moodbased Recommendation System

Abstract

In the current digital landscape, users frequently engage with various media platforms to consume entertainment such as music, movies, and books. However, existing recommendation systems typically fail to account for users' emotional states now of interaction, leading to generic recommendations that may not align with their immediate mood or preferences. The MoodJourney project addresses this critical gap by developing a sophisticated recommendation system designed to recognize and interpret the user's emotional context in real-time. MoodJourney leverages advanced machine learning techniques to accurately identify user emotions through comprehensive mood-mapping and labeling processes across diverse media datasets, including movies, music, and books. By integrating these emotional insights, MoodJourney provides personalized content recommendations that resonate more closely with the user's current emotional state, significantly enhancing user satisfaction and engagement. The implementation involves a hybrid recommendation approach, combining the strengths of both content-based and collaborative filtering methods, enriched further through the integration of knowledge graph technology. Extensive evaluation of the MoodJourney system has demonstrated substantial effectiveness, achieving commendable accuracy and reliability in generating emotionally relevant recommendations. Through its innovative approach, MoodJourney aims to transform the media recommendation experience, creating a more intuitive, dynamic, and emotionally attuned platform that truly understands and responds to user needs in real-time.