Advertisement

Meta-path Based Heterogeneous Graph Embedding for Music Recommendation

  • Qianqi Fang
  • Ling Liu
  • Junliang Yu
  • Junhao Wen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11303)

Abstract

The prosperous online music streaming industry makes personalized music recommendation a topic worthy of extensive study. Traditional music recommendation techniques which are based on conventional collaborative filtering or acoustic content features usually sufffer from data sparsity or time-consuming computation problems, respectively. In fact, online music services not only generate listening history for each user but also accumulate a large amount of heterogeneous data including performers, tags, ownerships and so on. Capturing underlying user preference from the heterogeneous data to enhance music recommendation is transparently promising, because on one hand these data can mitigate the sparsity of listening history while incorporating them into recommendation model is computationally affordable. To this end, in this paper we propose a novel music recommendation approach. It first models the music system as a heterogeneous music graph. Then, to make full use of the heterogeneous data, carefully designed meta-paths are used to dig up the information lying in the graph. Finally, we learn user preferences through a combination of Bayesian Personalized Ranking model and heterogeneous embedding representation learning. Extensive experimental analysis on real-world public dataset validates that the proposed approach outperforms the baselines, especially on cold start users.

Keywords

Music recommendation Heterogeneous graph Meta-path Embedding learning 

Notes

Acknowledgement

This research is supported by the Graduate Scientific Research and Innovation Foundation of Chongqing (cys17035), the National Natural Science Foundation of China (61472021).

References

  1. 1.
    Benzi, K., Kalofolias, V., Bresson, X., Vandergheynst, P.: Song recommendation with non-negative matrix factorization and graph total variation. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2439–2443 (2016)Google Scholar
  2. 2.
    Cheng, R., Tang, B.: A music recommendation system based on acoustic features and user personalities. In: Cao, H., Li, J., Wang, R. (eds.) PAKDD 2016. LNCS (LNAI), vol. 9794, pp. 203–213. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-42996-0_17CrossRefGoogle Scholar
  3. 3.
    Cheng, Z., Shen, J., Zhu, L., Kankanhalli, M., Nie, L.: Exploiting music play sequence for music recommendation. In: Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 3654–3660 (2017)Google Scholar
  4. 4.
    Dieleman, S., Schrauwen, B.: Deep content-based music recommendation. In: International Conference on Neural Information Processing Systems, pp. 2643–2651 (2013)Google Scholar
  5. 5.
    Dong, Y., Chawla, N.V., Swami, A.: metapath2vec: Scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 135–144 (2017)Google Scholar
  6. 6.
    Goyal, P., Ferrara, E.: Graph embedding techniques, applications, and performance: a survey. Knowl. Based Syst. 151, 78–94 (2018)CrossRefGoogle Scholar
  7. 7.
    Guo, C., Liu, X.: Automatic feature generation on heterogeneous graph for music recommendation. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 807–810 (2015)Google Scholar
  8. 8.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 263–272 (2008)Google Scholar
  9. 9.
    Jannach, D., Lerche, L., Kamehkhosh, I.: Beyond “hitting the hits”: generating coherent music playlist continuations with the right tracks. In: Proceedings of the Ninth ACM Conference on Recommender Systems, pp. 187–194. ACM (2015)Google Scholar
  10. 10.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  11. 11.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Uncertainty in Artificial Intelligence, pp. 452–461 (2009)Google Scholar
  12. 12.
    Rentfrow, P.J., Mcdonald, J.A., Oldmeadow, J.A.: You are what you listen to: young people’s stereotypes about music fans. Group Process. Intergroup Relat. 12(3), 329–344 (2009)CrossRefGoogle Scholar
  13. 13.
    Schedl, M., Knees, P., Gouyon, F.: New paths in music recommender systems research. In: The Eleventh ACM Conference, pp. 392–393. ACM (2017)Google Scholar
  14. 14.
    Schedl, M., Schnitzer, D.: Hybrid retrieval approaches to geospatial music recommendation. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 793–796 (2013)Google Scholar
  15. 15.
    Soleymani, M., Aljanaki, A., Wiering, F., Veltkamp, R.C.: Content-based music recommendation using underlying music preference structure. In: IEEE International Conference on Multimedia and Expo, pp. 1–6 (2015)Google Scholar
  16. 16.
    Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: Pathsim: meta path-based top-k similarity search in heterogeneous information networks. Proc. VLDB Endow. 4, 992–1003 (2011)Google Scholar
  17. 17.
    Vall, A., Skowron, M., Knees, P., Schedl, M.: Improving music recommendations with a weighted factorization of the tagging activity. In: ISMIR, pp. 65–71 (2015)Google Scholar
  18. 18.
    Wang, D., Deng, S., Zhang, X., Xu, G.: Learning music embedding with metadata for context aware recommendation. In: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, pp. 249–253 (2016)Google Scholar
  19. 19.
    Wang, D., Xu, G., Deng, S.: Music recommendation via heterogeneous information graph embedding. In: International Joint Conference on Neural Networks, pp. 596–603 (2017)Google Scholar
  20. 20.
    Wang, X., Wang, Y.: Improving content-based and hybrid music recommendation using deep learning. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 627–636 (2014)Google Scholar
  21. 21.
    Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., Sun, J.: Temporal recommendation on graphs via long-and short-term preference fusion. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 723–732 (2010)Google Scholar
  22. 22.
    Yu, J., Gao, M., Rong, W., Song, Y., Fang, Q., Xiong, Q.: Make users and preferred items closer: recommendation via distance metric learning. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.-S.M. (eds.) ICONIP 2017. LNCS, vol. 10638, pp. 297–305. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-70139-4_30CrossRefGoogle Scholar
  23. 23.
    Yu, J., Gao, M., Rong, W., Song, Y., Xiong, Q.: A social recommender based on factorization and distance metric learning. IEEE Access 5, 21557–21566 (2017)CrossRefGoogle Scholar
  24. 24.
    Yu, J., Gao, M., Song, Y., Zhao, Z., Rong, W., Xiong, Q.: Connecting factorization and distance metric learning for social recommendations. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds.) KSEM 2017. LNCS (LNAI), vol. 10412, pp. 389–396. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-63558-3_33CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Qianqi Fang
    • 1
    • 2
  • Ling Liu
    • 2
  • Junliang Yu
    • 2
  • Junhao Wen
    • 2
  1. 1.Ping An Trust Co., Ltd.ShenzhenChina
  2. 2.School of Big Data and Software EngineeringChongqing UniversityChongqingChina

Personalised recommendations