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Soft Computing

, Volume 22, Issue 3, pp 1023–1031 | Cite as

Session-aware music recommendation via a generative model approach

  • Zhao-quan Cai
  • Hui Hu
Methodologies and Application
  • 190 Downloads

Abstract

Music recommendation is a critical technology enabling users to overcome the overload of songs in the music sites. Although existing methods of similar retrieval or sequence prediction have attained success to some extend, one important factor has not been considered in the previous work, that is, users may change their music interest in different sessions. How to define the users’ current interest is still a problem. In this paper, we propose a topic-based probabilistic model for addressing this problem by developing session-aware latent topics to model the users’ listening behavior. The model is designed based on the insight that users’ listening behavior is subjected to not only their personal interest, but also to their special interest in the session. Specifically, when making recommendation, our model estimates the probability of the song selection based on the mixture of the two aspects with a weight-based scheme. We have conducted experiments on a real music dataset from Last.fm. The empirical results demonstrate that our model performs much better than other state-of-the-art methods.

Keywords

Music recommendation Recommender systems Generative model Topic model More 

Notes

Acknowledgements

This work was supported by the Distinguished Young Scholars Fund of Department of Education (No. Yq2013126) and Natural National Science Foundation of China (No. 61370185), Guangdong Natural Science Foundation (Nos. S2013010013432, S2013010015940) and the Science and Technology Project (Nos. 2014B050013016, 2014B020004023).

Compliance with ethical standards

Conflict of interest

Zhao-quan Cai, Hui Hu all declare that they have no conflict of interest

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Huizhou UniversityHuizhouChina

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