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Explicit Modelling of the Implicit Short Term User Preferences for Music Recommendation

  • Kartik GuptaEmail author
  • Noveen Sachdeva
  • Vikram Pudi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10772)

Abstract

Recommender systems are a key component of music sharing platforms, which suggest musical recordings a user might like. People often have implicit preferences while listening to music, though these preferences might not always be the same while they listen to music at different times. For example, a user might be interested in listening to songs of only a particular artist at some time, and the same user might be interested in the top-rated songs of a genre at another time. In this paper we try to explicitly model the short term preferences of the user with the help of Last.fm tags of the songs the user has listened to. With a session defined as a period of activity surrounded by periods of inactivity, we introduce the concept of a subsession, which is that part of the session wherein the preference of the user does not change much. We assume the user preference might change within a session and a session might have multiple subsessions. We use our modelling of the user preferences to generate recommendations for the next song the user might listen to. Experiments on the user listening histories taken from Last.fm indicate that this approach beats the present methodologies in predicting the next recording a user might listen to.

Keywords

Recommendation systems User modelling 

Notes

Acknowledgements

We would like to thank Alastair Porter for his valuable feedback on the initial drafts of this paper and improving the quality of this paper.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.IIIT-HHyderabadIndia

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