Advanced Music Recommendation System Leveraging Machine Learning for Personalized User Experience

Abstract

Music streaming services have made it easy to access a wide variety of music, but there is still room for improvement in customization and emotion-based suggestions. Users often rely on recommendation systems to find music that suits their current mood, but these systems may fall short if they don't account for changes in emotional states. This project focuses on developing a personalized music recommendation system that uses machine learning to analyze listeners’ thoughts, emotions, and facial expressions. By collecting and preprocessing user data, the system can extract features, select appropriate models, and generate recommendations that align with the user's unique tastes and current mood. The system's effectiveness is evaluated using metrics like accuracy, precision, recall, and F1 score, ensuring continuous improvement. Ultimately, this music recommendation system enhances the music discovery experience, helping users find new tracks they might not have encountered otherwise, while delivering a more engaging and personalized listening experience.