Exploiting and Exploring Hierarchical Structure in Music Recommendation

  • Kai Lu
  • Guanyuan Zhang
  • Rui Li
  • Shuai Zhang
  • Bin Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7675)


Collaborative Filtering (CF) approaches have been widely applied in music recommendation as they provide users with personalized song lists. However, CF methods usually suffer from severe sparsity problem which greatly affects their performance. Previous works mainly use music content information and other external resources to relieve it, while they ignore that music entities are multi-typed and items are tied together within a hierarchy. E.g., for a track, we can identify its album, artist and associated genres. Therefore, in this paper, we propose a framework which utilizes the hierarchical structure in two ways. On one side, we exploit the hierarchical links to find more reliable neighbors; On the other side, we explore the effect of hierarchical structure on users’ potential preferences. In a further step, we incorporate the two aspects seamlessly into an integrated model which could make use of the advantages of both sides. Experiments conducted on the large-scale Yahoo! Music datasets show: (1) our approach significantly improves the recommendation performance; (2) compared with baselines, our approach is much more powerful on the even sparser training data, demonstrating that our approach could effectively mitigate the sparsity issue.


Music Recommendation Collaborative Filtering Hierarchical Structure Neighborhood Model Latent Factor Model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Amatriain, X., Bonada, J., Loscos, À., Arcos, J.L., Verfaille, V.: Content-based transformations. Journal of New Music Research 32(1), 95–114 (2003)CrossRefGoogle Scholar
  2. 2.
    Logan, B.: Mel frequency cepstral coefficients for music modeling, pp. 723–732. ACM (2010)Google Scholar
  3. 3.
    Chen, H., Chen, A.: A music recommendation system based on music data grouping and user interests, 231–238 (2001)Google Scholar
  4. 4.
    Lamere, P.: Social tagging and music information retrieval. Journal of New Music Research 37(2), 101–114 (2008)CrossRefGoogle Scholar
  5. 5.
    Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: Proc. WWW 2001, pp. 285–295. ACM (2001)Google Scholar
  6. 6.
    Koren, Y.: The bellkor solution to the netflix grand prize (2009)Google Scholar
  7. 7.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proc. 14th International Conference on Knowledge Discovery and Data Mining. ACM (2008)Google Scholar
  8. 8.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: 8th IEEE International Conference on Data Mining, pp. 263–272. IEEE (2008)Google Scholar
  9. 9.
    Adomavicius, G., Tuzhilin, A.: Towards the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE TKDE 17(6), 734–749 (2005)Google Scholar
  10. 10.
    Bell, R.M., Koren, Y.: Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: ICDM, pp. 43–52. IEEE Computer Society (2007)Google Scholar
  11. 11.
    Funk, S.: Netflix update (2006),
  12. 12.
    Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD Cup and Workshop (2007)Google Scholar
  13. 13.
    Takács, G., Pilászy, I., Németh, B., Tikk, D.: Matrix factorization and neighbor based algorithms for the netflix prize problem. In: Proc. of the 2008 ACM Conference on Recommender Systems, pp. 267–274. ACM (2008)Google Scholar
  14. 14.
    Sarwar, B., Karypis, G., Konstan, J.A., Riedl, J.: Application of dimensionality reduction in recommender systemca case study (2000)Google Scholar
  15. 15.
    Shao, B., Wang, D., Li, T., Ogihara, M.: Music recommendation based on acoustic features and user access patterns, 1602–1611Google Scholar
  16. 16.
    Dror, G., Koenigstein, N., Koren, Y.: Yahoo! music recommendations: Modeling music ratings with temporal dynamics and item taxonomy. In: Proc. 5th ACM Conference on Recommender Systems, pp. 165–172 (2011)Google Scholar
  17. 17.
    Chen, T., Zheng, Z., Lu, Q.: Informative ensemble of multiresolution dynamic factorization models (2011)Google Scholar
  18. 18.
    Dror, G., Koenigstein, N., Koren, Y.: The yahoo! music dataset and kdd-cup11. In: KDD-Cup Workshop (2011)Google Scholar
  19. 19.
    Chen, P., Tsai, C.Y., et al.: A linear ensemble of individual and blended models for music rating prediction. In: KDD-Cup Workshop (2011)Google Scholar
  20. 20.
    Jahrer, M.,Töscher, A.: Collaborative filtering ensemble. In: KDD-Cup Workshop (2011)Google Scholar
  21. 21.
    Töscher, A., Jahrer, M., Bell, R.: The bigchaos solution to the netflix grand prize. Netflix Prize Documentation (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kai Lu
    • 1
  • Guanyuan Zhang
    • 1
  • Rui Li
    • 1
  • Shuai Zhang
    • 1
  • Bin Wang
    • 1
  1. 1.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina

Personalised recommendations