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DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation.
Elad
Liebman, Maytal Saar-Tsechansky, and Peter
Stone.
In Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS),
May 2015.
[PDF]1.5MB [postscript]38.4MB [slides.pdf]2.6MB
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.
@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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