A Personalized Music Recommendation System based on User Moods

Considering millions of songs available in online streaming services, it is difficult to identify the most suitable song for a specific user. Most of the music recommendation systems are based on user ratings and acoustic features of the songs. Those systems are unable to address the cold start problem and rating diversity. Furthermore, song preferences will be altering based on the current mood of the users. If these problems were not addressed, then these online services will fail to achieve user satisfaction. To cope with those problems, this paper proposes a novel music recommendation approach that utilizes social media content such as posts, comments, interactions, etc. and recommend them with the most relevant songs to relax their mind considering the current mood (happy, sad, calm and angry).