A Context-Aware Music Recommendation System Based On Emotion

Music recommendation systems are emerging applications that attempt to provide music to suit users’ needs or moods. To achieve this goal, traditional recommendation techniques are widely used in this field. Most of the music recommendation system exploits user interest, metadata, listening history, and audio signals of music to generate a personalized function that can predict songs the user may like.owever, listening experience is a type of subjective cognitive experience that is highly dependent on the individual’s intention at a particular time. Thus, contexts such as time, location, weather, and temperature have been added to systems to improve their accuracy. Psychological influences represent another important aspect that determines the user’s satisfaction with the recommended results.n the proposed approach, listeners’ emotional information is used in conjunction with context information. We first gather the explicit similarity between human, emotion, context, and music based on Kate Hevner’s Adjective Cycle, the semantic network of ConceptNet, and musicology as the common fundamental. Then, we adjust the individual differences according to the user’s musical taste, listening behavior, and feedback through user-based collaborative filtering in order to generate a more individual intentional music recommendation system.