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arXiv · 1401.1880

DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation

Elad Liebman·Maytal Saar-Tsechansky·Peter Stone
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Abstract

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. A fundamental aspect of music perception is that music is experienced in temporal context and in sequence. In this work we present DJ-MC, a novel reinforcement-learning framework for music recommendation that does not recommend songs individually but rather song sequences, or playlists, based on a model of preferences for both songs and song transitions. The model is learned online and is uniquely adapted for each listener. To reduce exploration time, DJ-MC exploits user feedback to initialize a model, which it subsequently updates by reinforcement. We evaluate our framework with human participants using both real song and playlist data. Our results indicate that DJ-MC's ability to recommend sequences of songs provides a significant improvement over more straightforward approaches, which do not take transitions into account.

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Elad Liebman, Maytal Saar-Tsechansky, Peter Stone. 2014-01-09. DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation. https://arxiv.org/abs/1401.1880

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