Soft Computing

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

Session-aware music recommendation via a generative model approach

  • Zhao-quan CaiEmail author
  • Hui Hu
Methodologies and Application


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 The empirical results demonstrate that our model performs much better than other state-of-the-art methods.


Music recommendation Recommender systems Generative model Topic model More 



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.


  1. Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749CrossRefGoogle Scholar
  2. Almohammadi K, Hagras H, Yao B, Alzahrani A, Alghazzawi D, Aldabbagh G (2015) A type-2 fuzzy logic recommendation system for adaptive teaching. Soft Comput 1–15. doi: 10.1007/s00500-015-1826-y
  3. Aucouturier JJ, Pachet F (2002) Music similarity measures: What’s the use? In: Proceedings of 3rd international conference on music information retrieval 2003. IRCAM, Paris, pp 157–163Google Scholar
  4. Berenzweig A, Logan B, Ellis DPW, Whitman B (2003) A large-scale evaluation of acoustic and subjective music similarity measures. Comput Music J 28(2):63–76CrossRefGoogle Scholar
  5. Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3: 993–1022.
  6. Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the fourteenth conference on uncertainty in artificial intelligence, UAI’98. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 43–52.
  7. Brown PF, deSouza PV, Mercer RL, Pietra VJD, Lai JC (1992) Class-based n-gram models of natural language. Comput Linguist 18(4): 467–479.
  8. Bugaychenko D, Dzuba A (2013) Musical recommendations and personalization in a social network. In: Proceedings of the 7th ACM conference on recommender systems, RecSys’13. ACM, New York, NY, USA, pp 367–370. doi: 10.1145/2507157.2507192
  9. Casey M, Veltkamp R, Goto M, Leman M, Rhodes C, Slaney M (2008) Content-based music information retrieval: current directions and future challenges. Proc IEEE 96(4):668–696. doi: 10.1109/JPROC.2008.916370 CrossRefGoogle Scholar
  10. Celma O (2010) Music recommendation and discovery: the long tail, long fail, and long play in the digital music space. Springer, New YorkCrossRefGoogle Scholar
  11. Chen S, Moore JL, Turnbull D, Joachims T (2012) Playlist prediction via metric embedding. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’12. ACM, New York, NY, USA, pp 714–722. doi: 10.1145/2339530.2339643
  12. Clifford R, Iliopoulos C (2004) Approximate string matching for music analysis. Soft Comput 8(9):597–603CrossRefzbMATHGoogle Scholar
  13. Gaeta M, Orciuoli F, Rarit L, Tomasiello S (2016) Fitted q-iteration and functional networks for ubiquitous recommender systems. Soft Comput 1–9. doi: 10.1007/s00500-016-2248-1
  14. Gu B, Sheng VS, Tay KY, Romano W, Li S (2014) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416MathSciNetCrossRefGoogle Scholar
  15. Gu B, Sheng VS, Wang Z, Ho D, Osman S, Li S (2015) Incremental learning for v-support vector regression. Neural Netw 67:140CrossRefGoogle Scholar
  16. Gu B, Sun X, Sheng VS (2016) Structural minimax probability machine. IEEE Trans Neural Netw Learn Syst 1–11 PP(99):1–11. doi: 10.1109/TNNLS.2016.2544779
  17. Guo L, Ma J, Chen Z, Zhong H (2014) Learning to recommend with social contextual information from implicit feedback. Soft Comput 19(5):1351–1362CrossRefGoogle Scholar
  18. Hariri N, Mobasher B, Burke R (2012) Context-aware music recommendation based on latenttopic sequential patterns. In: Proceedings of the sixth ACM conference on recommender systems, RecSys’12. ACM, New York, NY, USA, pp 131–138. doi: 10.1145/2365952.2365979
  19. Hofmann T (1999) Probabilistic latent semantic indexing. In: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval, SIGIR’99. ACM, New York, NY, USA, pp 50–57. doi: 10.1145/312624.312649
  20. Hofmann T (2004) Latent semantic models for collaborative filtering. ACM Trans Inf Syst 22(1):89–115. doi: 10.1145/963770.963774 CrossRefGoogle Scholar
  21. Jin R, Chai JY, Si L (2004) An automatic weighting scheme for collaborative filtering. In: Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval, SIGIR’04. ACM, New York, NY, USA, pp 337–344. doi: 10.1145/1008992.1009051
  22. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD’08. ACM, New York, NY, USA, pp 426–434. doi: 10.1145/1401890.1401944
  23. Kosmides P, Demestichas K, Adamopoulou E, Remoundou C, Loumiotis I, Theologou M, Anagnostou M (2016) Providing recommendations on location-based social networks. J Ambient Intell Human Comput 7(4):567–578. doi: 10.1007/s12652-016-0346-7
  24. Li J, Chen X, Li M, Li J, Lee PPC, Lou W (2014) Secure deduplication with efficient and reliable convergent key management. IEEE Trans Parallel Distrib Syst 25(6):1615–1625CrossRefGoogle Scholar
  25. Li J, Huang X, Li J, Chen X, Xiang Y (2014) Securely outsourcing attribute-based encryption with checkability. IEEE Trans Parallel Distrib Syst 25(8):2201–2210CrossRefGoogle Scholar
  26. Ma T, Zhou J, Tang M, Tian Y, Al-Dhelaan A, Al-Rodhaan M, Lee S (2015) Social network and tag sources based augmenting collaborative recommender system. Ieice Trans Inf Syst E98.D(4):902–910CrossRefGoogle Scholar
  27. Marlin B (2004) Modeling user rating profiles for collaborative filtering. In: Thrun S, Saul L, Schölkopf B (eds) Advances in neural information processing systems, vol 16. MIT Press, CambridgeGoogle Scholar
  28. McFee B, Bertin-Mahieux T, Ellis DP, Lanckriet GR (2012) The million song dataset challenge. In: Proceedings of the 21st international conference companion on World Wide Web, WWW’12 companion. ACM, New York, NY, USA, pp 909–916. doi: 10.1145/2187980.2188222
  29. McFee B, Lanckriet GRG (2011) The natural language of playlists. In: Proceedings of the 12th international society for music information retrieval conference. Miami, pp 537–541Google Scholar
  30. Nilashi M, Ibrahim OB, Ithnin N, Zakaria R (2015) A multi-criteria recommendation system using dimensionality reduction and neuro-fuzzy techniques. Soft Comput 19(11):1–35CrossRefGoogle Scholar
  31. Rosen-Zvi M, Griffiths T, Steyvers M, Smyth P (2004) The author-topic model for authors and documents. In: Proceedings of the 20th conference on uncertainty in artificial intelligence, UAI’04. AUAI Press, Arlington, Virginia, United States, pp 487–494.
  32. Schedl M, Schnitzer D (2013) Hybrid retrieval approaches to geospatial music recommendation. In: Proceedings of the 36th international ACM SIGIR conference on research and development in information retrieval, SIGIR’13. ACM, New York, NY, USA, pp 793–796. doi: 10.1145/2484028.2484146
  33. Srebro N, Jaakkola T (2003) Weighted low-rank approximations. In: Proceedings of the twentieth international conference on machine learning (ICML-2003). AAAI Press, Washington DC, pp 720–727Google Scholar
  34. Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 4:2. doi: 10.1155/2009/421425 Google Scholar
  35. Veloz T, Razeto P (2015) The state context property formalism: from concept theory to the semantics of music. Soft Comput 1–9. doi: 10.1007/s00500-015-1914-z
  36. Wang J, de Vries AP, Reinders MJT (2006) Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR’06. ACM, New York, NY, USA, pp 501–508. doi: 10.1145/1148170.1148257
  37. Wang M, Ma J (2015) A novel recommendation approach based on users weighted trust relations and the rating similarities. Soft Comput 20(10):3981–3990. doi: 10.1007/s00500-015-1734-1
  38. Wang X, Rosenblum D, Wang Y (2012) Context-aware mobile music recommendation for daily activities. In: Proceedings of the 20th ACM international conference on multimedia, MM’12. ACM, New York, NY, USA, pp 99–108. doi: 10.1145/2393347.2393368
  39. Wen X, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295:395–406CrossRefGoogle Scholar
  40. Wu X, Liu Q, Chen E, He L, Lv J, Cao C, Hu G (2013) Personalized next-song recommendation in online karaokes. In: Proceedings of the 7th ACM conference on recommender systems, RecSys’13. ACM, New York, NY, USA, pp 137–140. doi: 10.1145/2507157.2507215
  41. Xie S, Wang Y (2014) Construction of tree network with limited delivery latency in homogeneous wireless sensor networks. Wirel Pers Commun 78(78):231–246CrossRefGoogle Scholar
  42. Yang D, Chen T, Zhang W, Lu Q, Yu Y (2012) Local implicit feedback mining for music recommendation. In: Proceedings of the Sixth ACM conference on recommender systems, RecSys’12. ACM, New York, NY, USA, pp 91–98. doi: 10.1145/2365952.2365973

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Huizhou UniversityHuizhouChina

Personalised recommendations