DJ-MC: A Reinforcement-Learning Framework for a Music Playlist

In recent years, there has been growing focus on the study of automated recommender systems. Music recommendation systems serve as a prominent domain for such works, both from an academic and a commercial perspective. To our knowledge, most of these systems focus on predicting the preference of individual songs independently based on a learned model of a listener. However, a relatively well known fact in music cognition is that music is experienced in temporal context and in sequence. In this work we present a reinforcement-learning based framework for music recommendation that does not recommend songs individually but rather song sequences, or playlists, based on a learned model of preferences for both individual songs and song transitions. To reduce exploration time, we initialize a model based on user feedback. This model is subsequently updated by reinforcement. We show our algorithm outperforms a more naive approach both on synthetic data and on a real song database.