Music Playlist Recommendation Using Acoustic-Feature Transitions

Music is important in our daily life not only for entertainment but also for mental health. When listening to music, playlists are used to eliminate the need for individual selection. The creation of playlist is difficult and tedious for users and has been the topic of research in many studies. However, many proposed playlist generation methods are based on either similar acoustic features or meta-data similarities. In this study, we propose a new method for music playlist recommendation using acoustic feature transitions where the next song will be selected such that it naturally transitions from the current song. Our preliminary evaluations show that the proposed method is more effective compared with other methods such as random selection and nearest neighbor methods