RankMBPR: Rank-Aware Mutual Bayesian Personalized Ranking for Item Recommendation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9658)

Abstract

Previous works indicated that pairwise methods are state-of- the-art approaches to fit users’ taste from implicit feedback. In this paper, we argue that constructing item pairwise samples for a fixed user is insufficient, because taste differences between two users with respect to a same item can not be explicitly distinguished. Moreover, the rank position of positive items are not used as a metric to measure the learning magnitude in the next step. Therefore, we firstly define a confidence function to dynamically control the learning step-size for updating model parameters. Sequently, we introduce a generic way to construct mutual pairwise loss from both users’ and items’ perspective. Instead of user-oriented pairwise sampling strategy alone, we incorporate item pairwise samples into a popular pairwise learning framework, bayesian personalized ranking (BPR), and propose mutual bayesian personalized ranking (MBPR) method. In addition, a rank-aware adaptively sampling strategy is proposed to come up with the final approach, called RankMBPR. Empirical studies are carried out on four real-world datasets, and experimental results in several metrics demonstrate the efficiency and effectiveness of our proposed method, comparing with other baseline algorithms.

References

  1. 1.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434. ACM (2008)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    Rendle, S., Freudenthaler, C.: Improving pairwise learning for item recommendation from implicit feedback. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp. 273–282. ACM (2014)Google Scholar
  4. 4.
    Pan, R., Zhou, Y., Cao, B., Liu, N.N., Lukose, R., Scholz, M., Yang, Q.: One-class collaborative filtering. In: Eighth IEEE International Conference on Data Mining, pp. 502–511. IEEE (2008)Google Scholar
  5. 5.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Eighth IEEE International Conference on Data Mining, pp. 263–272. IEEE (2008)Google Scholar
  6. 6.
    Shi, Y., Larson, M., Hanjalic, A.: List-wise learning to rank with matrix factorization for collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 269–272. ACM (2010)Google Scholar
  7. 7.
    Sebastian, R., Limin, Y., Andrew, M.: Relation extraction with matrix factorization and universal schemas. In: Joint Human Language Technology Conference/Annual Meeting of the North Computational Linguistics (HLT-NAACL) (2013)Google Scholar
  8. 8.
    Pan, W., Chen, L.: GBPR: group preference based bayesian personalized ranking for one-class collaborative filtering. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI, pp. 2691–2697 (2013)Google Scholar
  9. 9.
    Rendle, S., Schmidt-Thieme, L.: Pairwise interaction tensor factorization for personalized tag recommendation. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 81–90. ACM (2010)Google Scholar
  10. 10.
    Weston, J., Bengio, S., Usunier, N.: Large scale image annotation: learning to rank with joint word-image embeddings. Mach. Learn. 81(1), 21–35 (2010)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Weston, J., Yee, H., Weiss, R.J.: Learning to rank recommendations with the k-order statistic loss. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 245–248. ACM (2013)Google Scholar
  12. 12.
    Weston, J., Wang, C., Weiss R., Berenzweig A.: In: Proceedings of the 29th International Conference on Machine Learning, pp. 9–16. ACM (2012)Google Scholar
  13. 13.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of International Conference on the World Wide Web, pp. 285–295. ACM (2001)Google Scholar
  14. 14.
    Celma, O.: Music Recommendation and Discovery in the Long Tail. Ph.D. thesis, Universitat Pompeu Fabra, Barcelona, Spain (2008)Google Scholar
  15. 15.
    Das, A.S., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: Proceedings of the 16th International Conference on World Wide Web, pp. 271–280. ACM (2007)Google Scholar
  16. 16.
    Qiu, H., Zhang, C., Miao, J.: Pairwise one class recommendation algorithm. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS, vol. 9078, pp. 744–755. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  17. 17.
    Qu, M., Qiu, G., He, X., Zhang, C., Wu, H., Bu, J., Chen, C.: Probabilistic question recommendation for question answering communities. In: Proceedings of the 18th International Conference on World Wide Web, pp. 1229–1230. ACM (2009)Google Scholar
  18. 18.
    Liu, Y., Zhao, P., Sun, A., Miao, C.: A boosting algorithm for item recommendation with implicit feedback. In: Proceedings of International Joint Conference on Artificial Intelligence, IJCAI (2015)Google Scholar
  19. 19.
    Zhao, T., Mcauley, J., King, I.: Leveraging social connections to improve personalized ranking for collaborative filtering. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, pp. 261–270. ACM (2014)Google Scholar
  20. 20.
    Zhao, T., McAuley, J., King, I.: Improving latent factor models via personalized feature projection for one-class recommendation. In: Proceedings of the 24th International Conference on Information and Knowledge Management. ACM (2015)Google Scholar
  21. 21.
    Yu, X., Ren, X., Sun, Y., Gu, Q., Sturt, B., Khandelwal, U., Norick, B., Han, J.: Personalized entity recommendation: a heterogeneous information network approach. In: Proceedings of the 7th International Conference on Web Search and Data Mining, pp. 283–292. ACM (2014)Google Scholar
  22. 22.
    Chen, K., Chen, T., Zheng, G., Jin, O., Yao, E., Yu, Y.: Collaborative personalized tweet recommendation. In: Proceedings of the 35th International SIGIR Conference on Research and Development in Information Retrieval, pp. 661–670. ACM (2012)Google Scholar
  23. 23.
    Chen, C., Yin, H., Yao, J., Cui, B.: TeRec: a temporal recommender system over tweet stream. In: Proceedings of the 39th International Conference on Very Large Data Bases, pp. 1254–1257. ACM (2012)Google Scholar
  24. 24.
    Yin, H., Sun, Y., Cui, B., Hu, Z., Chen, L.: LCARS: a location-content-aware recommender system. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 221–229. ACM (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Alibaba Research Centre for Complexity SciencesHangzhou Normal UniversityHangzhouChina
  2. 2.Department of Computer ScienceRutgers UniversityBrunswickUSA
  3. 3.Web Sciences CentreUniversity of Electronic Science and Technology of ChinaChengduChina

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