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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Oord, A.V.D., Dieleman, S., Schrauwen, B.: Deep content-based music recommendation. In: NIPS, pp. 2643–2651 (2013)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, WWW 2001, New York, pp. 285–295 (2001)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: Proceedings of ICLR 2016 (2016)
McFee, B., Barrington, L., Lanckriet, G.: Learning content similarity for music recommendation. IEEE Trans. Audio Speech Lang. Process. 20(8), 2207–2218 (2012)
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper. In: SIGIR 1999 Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, CA (1999)
Liang, D., Zhan, M., Ellis, D.P.: Content-aware collaborative music recommendation using pre-trained neural networks. In: Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR 2015, Malaga, Spain, 26–30 October 2015
Kuo, F.F., Shan, M.K.: A personalized music filtering system based on melody style classification. In: 2002 IEEE International Conference on Data Mining, Proceedings, pp. 649–652 (2002)
Hariri, N., Mobasher, B., Burke, R.: Context-aware music recommendation based on latenttopic sequential patterns. In: Sixth ACM Conference on Recommender Systems, Dublin, pp. 131–138 (2012)
Last.fm: http://www.last.fm. Accessed 23 Apr 2017
Last.fm-dataset-1k-users. http://www.dtic.upf.edu/ocelma/MusicRecommendationDataset/lastfm-1K.html. Accessed 23 Apr 2017
Devooght, R., Bersini, H.: Long and short-term recommendations with recurrent neural networks. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 13–21 (2017)
Pagare, R., Naser, I., Pingale, V., Wathap, N.: Enhancing collaborative filtering in music recommender system by using context based approach
Brafman, R.I., Heckerman, D., Shani, G.: Recommendation as a stochastic sequential decision problem. In: ICAPS, pp. 164–173 (2003)
Dias, R., Fonseca, M.J.: Improving music recommendation in session-based collaborative filtering by using temporal context. In: International Conference on Tools with Artificial Intelligence, pp. 783–788. IEEE (2013)
Park, S.E., Lee, S., Lee, S.G.: Session-based collaborative filtering for predicting the next song. In: 2011 First ACIS/JNU International Conference on Computers, Networks, Systems and Industrial Engineering, pp. 353–358 (2011)
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: WWW, pp. 811–820. ACM (2010)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461. AUAI Press (2009)
Suglia, A., Greco, C., Musto, C., de Gemmis, M., Lops, P., Semeraro, G.: A deep architecture for content-based recommendations exploiting recurrent neural networks. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 202–211. ACM (2017)
Wan, S., Lan, Y., Wang, P., Guo, J., Xu, J., Cheng, X.: Next basket recommendation with neural networks. In: Poster Proceedings of RecSys 2015 (2015)
Tanimoto, T.: An elementary mathematical theory of classification and prediction. In: Internal IBM Technical report (1958)
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. JMLR 3, 1137–1155 (2003)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM, pp. 263–272 (2008)
Yoshii, K., Goto, M., Komatani, K., Ogata, T., Okuno, H.G.: Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences. In: Proceedings of the International Conference on Music Information Retrieval (2006)
Zhang, Y., Dai, H., Xu, C., Feng, J., Wang, T., Bian, J., Wang, B., Liu, T.: Sequential click prediction for sponsored search with recurrent neural networks. In: AAAI, pp. 1369–1375 (2014)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Gupta, K., Sachdeva, N., Pudi, V. (2018). Explicit Modelling of the Implicit Short Term User Preferences for Music Recommendation. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-76941-7_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76940-0
Online ISBN: 978-3-319-76941-7
eBook Packages: Computer ScienceComputer Science (R0)