The Right Music at the Right Time: Adaptive Personalized Playlists Based on Sequence Modeling1
Authors: Liebman, Elad; Saar-Tsechansky, Maytal; Stone, Peter
Journal: MIS Quarterly (2019)
<jats:p>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 e…