Music Playlist Generation Based on Reinforcement Learning Using Acoustic Feature Map

With the spread of streaming services that make playlist-based recommendation, automatic music playlist generation has been actively researched. Traditionally, most of music playlist generation methods have focused on the sounds and the genres of songs that have been listened by a user. However, users are bored and unsatisfied with the music playlist that fits users' preferences overly. In this paper, we propose a new music playlist generation method based on reinforcement learning using an acoustic feature map. Our reinforcement learning-based music playlist generation can overlook the whole songs since an agent explores all of the features on the map. This new playlist generation can achieve high diversity and smooth track transitions. Experimental results show the effectiveness of the proposed method.