Peter Stone's Selected Publications

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

Elad Liebman, Maytal Saar-Tsechansky, and Peter Stone. DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation. In Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2015.

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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.

BibTeX Entry

@InProceedings{AAMAS2015-eladlieb,
  author = {Elad Liebman and Maytal Saar-Tsechansky and Peter Stone},
  title = {DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation},
  booktitle = {Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
  location = {Istanbul, Turkey},
  month = {May},
  year = {2015},
  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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