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The right music at the right time: adaptive personalized playlists based on sequence modeling.
Elad
Liebman, Maytal Saar-Tsechansky, and Peter
Stone Peter Stone.
Management Information Systems Quarterly, 43(3):765–786, Society for Information Management
and The Management Information Systems Research Center, 2019.
Available from publisher's
website.
Recent years have seen a growing focus on automated personalized services, with music recommendations a particularly prominent domain for such contributions. However, while most prior work on music recommender systems has focused on preferences for songs and artists, a fundamental aspect of human music perception is that music is experienced in a temporal context and in sequence. Hence, listeners’ preferences also may be affected by the sequence in which songs are being played and the corresponding song transitions. Moreover, a listener’s sequential preferences may vary across circumstances, such as in response to different emotional or functional needs, so that different song sequences may be more satisfying at different times. It is therefore useful to develop methods that can learn and adapt to individuals’ sequential preferences in real time, so as to adapt to a listener’s contextual preferences during a listening session. Prior work on personalized playlists either considered batch learning from large historical data sets, attempted to learn preferences for songs or artists irrespective of the sequence in which they are played, or assumed that adaptation occurs over extended periods of time. Hence, this prior work did not aim to adapt to a listener’s current song and sequential prefer- ences in real time, during a listening session. This paper develops and evaluates a novel framework for online learning of and adaptation to a listener’s current song and sequence preferences exclusively by interacting with the listener, during a listening session. We evaluate the framework using both real playlist datasets and an experiment with human listeners. The results establish that the framework effectively learns and adapts to a listeners’ transition preferences during a listening session, and that it yields a significantly better listener experience. Our research also establishes that future advances of online adaptation to listener’s temporal preferences is a valuable avenue for research, and suggests that similar benefits may be possible from exploring online learning of temporal preferences for other personalized services.
@article{MISQ2019-eladlieb,
title={The right music at the right time: adaptive personalized playlists based on sequence modeling},
author={Elad Liebman and Maytal Saar-Tsechansky and Peter Stone Peter Stone},
journal={Management Information Systems Quarterly},
pages={765--786},
volume={43},
number={3},
year={2019},
publisher={Society for Information Management and The Management Information Systems Research Center},
abstract={
Recent years have seen a growing focus on automated personalized services, with music recommendations a
particularly prominent domain for such contributions. However, while most prior work on music recommender
systems has focused on preferences for songs and artists, a fundamental aspect of human music perception is
that music is experienced in a temporal context and in sequence. Hence, listeners’ preferences also may be
affected by the sequence in which songs are being played and the corresponding song transitions. Moreover,
a listener’s sequential preferences may vary across circumstances, such as in response to different emotional
or functional needs, so that different song sequences may be more satisfying at different times. It is therefore
useful to develop methods that can learn and adapt to individuals’ sequential preferences in real time, so as
to adapt to a listener’s contextual preferences during a listening session. Prior work on personalized playlists
either considered batch learning from large historical data sets, attempted to learn preferences for songs or
artists irrespective of the sequence in which they are played, or assumed that adaptation occurs over extended
periods of time. Hence, this prior work did not aim to adapt to a listener’s current song and sequential prefer-
ences in real time, during a listening session. This paper develops and evaluates a novel framework for online
learning of and adaptation to a listener’s current song and sequence preferences exclusively by interacting with
the listener, during a listening session. We evaluate the framework using both real playlist datasets and an
experiment with human listeners. The results establish that the framework effectively learns and adapts to a
listeners’ transition preferences during a listening session, and that it yields a significantly better listener
experience. Our research also establishes that future advances of online adaptation to listener’s temporal
preferences is a valuable avenue for research, and suggests that similar benefits may be possible from
exploring online learning of temporal preferences for other personalized services.},
wwwnote={Available from <a href="https://misq.org/the-right-music-at-the-right-time-adaptive-personalized-playlists-based-on-sequence-modeling.html">publisher's website</a>.},
}
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