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World Wide Web

, Volume 22, Issue 3, pp 1099–1129 | Cite as

Dual-regularized one-class collaborative filtering with implicit feedback

  • Yuan YaoEmail author
  • Hanghang Tong
  • Guo Yan
  • Feng Xu
  • Xiang Zhang
  • Boleslaw K. Szymanski
  • Jian Lu
Article
  • 165 Downloads
Part of the following topical collections:
  1. Special Issue on Geo-Social Computing

Abstract

Collaborative filtering plays a central role in many recommender systems. While most of the existing collaborative filtering methods are proposed for the explicit, multi-class settings (e.g., 1-5 stars in movie recommendation), many real-world applications actually belong to the one-class setting where user feedback is implicitly expressed (e.g., views in news recommendation and video recommendation). In this article, we propose dual-regularized one-class collaborative filtering models for implicit feedback. In particular, by dividing existing methods into point-wise class and pair-wise class, we first propose a point-wise model by integrating two existing methods and further exploiting the side information from both users and items. Next, we propose to add dual regularization into an existing pair-wise method with a different treatment of the side information. We also propose efficient algorithms to solve the proposed models. Extensive experimental evaluations on three real data sets demonstrate the effectiveness and efficiency of the proposed methods.

Keywords

Recommender systems One-class collaborative filtering Implicit feedback Dual regularization 

Notes

Acknowledgments

This work is supported by the National Key Research and Development Program of China (No. 2017YFB1001801), the National Natural Science Foundation of China (No. 61690204, 61672274, 61702252), and the Collaborative Innovation Center of Novel Software Technology and Industrialization. Hanghang Tong is partially supported by NSF (IIS-1651203, IIS-1715385, CNS-1629888 and IIS-1743040), DTRA (HDTRA1-16-0017), ARO (W911NF-16-1-0168), and gifts from Huawei and Baidu.

References

  1. 1.
    Anand, S.S., Griffiths, N.: A market-based approach to address the new item problem. In: Proceedings of the fifth ACM conference on recommender systems, pp. 205–212 (2011)Google Scholar
  2. 2.
    Bogdanov, P., Busch, M., Moehlis, J., Singh, A.K., Szymanski, B.K.: Modeling individual topic-specific behavior and influence backbone networks in social media. Soc. Netw. Anal. Min. 4(1), 204 (2014)CrossRefGoogle Scholar
  3. 3.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: UAI, pp. 43–52 (1998)Google Scholar
  4. 4.
    Chapelle, O., Sindhwani, V., Keerthi, S.S.: Optimization techniques for semi-supervised support vector machines. J. Mach. Learn. Res. 9, 203–233 (2008)zbMATHGoogle Scholar
  5. 5.
    Chen, H.C., Chen, A.L.: A music recommendation system based on music data grouping and user interests. In: CIKM, pp. 231–238 (2001)Google Scholar
  6. 6.
    Das, A.S., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: WWW, pp. 271–280 (2007)Google Scholar
  7. 7.
    Ding, C., Li, T., Peng, W., Park, H.: Orthogonal nonnegative matrix t-factorizations for clustering. In: KDD, pp. 126–135 (2006)Google Scholar
  8. 8.
    Du, L., Li, X., Shen, Y.D.: User graph regularized pairwise matrix factorization for item recommendation. In: Advanced data mining and applications, pp. 372–385 (2011)Google Scholar
  9. 9.
    Elo, A.E.: The rating of chessplayers past and present (1978)Google Scholar
  10. 10.
    Fang, Y., Si, L.: Matrix co-factorization for recommendation with rich side information and implicit feedback. In: Hetrec, pp. 65–69 (2011)Google Scholar
  11. 11.
    Gantner, Z., Drumond, L., Freudenthaler, C., Rendle, S., Schmidt-Thieme, L.: Learning attribute-to-feature mappings for cold-start recommendations. In: ICDM, pp. 176–185 (2010)Google Scholar
  12. 12.
    Gu, Q., Zhou, J., Ding, C.H.: Collaborative filtering: weighted nonnegative matrix factorization incorporating user and item graphs. In: SDM, pp. 199–210. SIAM (2010)Google Scholar
  13. 13.
    Hacker, S., Von Ahn, L.: Matchin: eliciting user preferences with an online game. In: CHI, pp. 1207–1216. ACM (2009)Google Scholar
  14. 14.
    Harpale, A.S., Yang, Y.: Personalized active learning for collaborative filtering. In: SIGIR, pp. 91–98. ACM (2008)Google Scholar
  15. 15.
    He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: WWW, pp. 507–517 (2016)Google Scholar
  16. 16.
    He, X., Zhang, H., Kan, M.Y., Chua, T.S.: Fast matrix factorization for online recommendation with implicit feedback. In: SIGIR, pp. 549–558. ACM (2016)Google Scholar
  17. 17.
    Herschtal, A., Raskutti, B.: Optimising area under the roc curve using gradient descent. In: ICML, p. 49 (2004)Google Scholar
  18. 18.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM, pp. 263–272 (2008)Google Scholar
  19. 19.
    Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Recsys, pp. 135–142. ACM (2010)Google Scholar
  20. 20.
    Jiang, M., Cui, P., Wang, F., Yang, Q., Zhu, W., Yang, S.: Social recommendation across multiple relational domains. In: CIKM, pp. 1422–1431. ACM (2012)Google Scholar
  21. 21.
    Kabbur, S., Ning, X., Karypis, G.: Fism: factored item similarity models for top-N recommender systems. In: KDD, pp. 659–667. ACM (2013)Google Scholar
  22. 22.
    Kanagal, B., Ahmed, A., Pandey, S., Josifovski, V., Yuan, J., Garcia-Pueyo, L.: Supercharging recommender systems using taxonomies for learning user purchase behavior. Proceedings of the VLDB Endowment 5(10), 956–967 (2012)CrossRefGoogle Scholar
  23. 23.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD, pp. 426–434. ACM (2008)Google Scholar
  24. 24.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  25. 25.
    Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: NIPS, pp. 556–562 (2000)Google Scholar
  26. 26.
    Li, Y., Hu, J., Zhai, C., Chen, Y.: Improving one-class collaborative filtering by incorporating rich user information. In: CIKM, pp. 959–968 (2010)Google Scholar
  27. 27.
    Ma, H.: An experimental study on implicit social recommendation. In: SIGIR, pp. 73–82. ACM (2013)Google Scholar
  28. 28.
    Ma, H., Yang, H., Lyu, M.R., King, I.: Sorec: social recommendation using probabilistic matrix factorization. In: CIKM, pp. 931–940 (2008)Google Scholar
  29. 29.
    Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: WSDM, pp. 287–296. ACM (2011)Google Scholar
  30. 30.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: Homophily in social networks. Annu. Rev. Sociol. 27(1), 415–444 (2001)CrossRefGoogle Scholar
  31. 31.
    Pan, R., Scholz, M.: Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering. In: KDD, pp. 667–676 (2009)Google Scholar
  32. 32.
    Pan, R., Zhou, Y., Cao, B., Liu, N.N., Lukose, R., Scholz, M., Yang, Q.: One-class collaborative filtering. In: ICDM, pp. 502–511 (2008)Google Scholar
  33. 33.
    Pan, W., Chen, L.: Gbpr: group preference based bayesian personalized ranking for one-class collaborative filtering. In: IJCAI, pp. 2691–2697 (2013)Google Scholar
  34. 34.
    Pan, W., Liu, M., Zhong, M.: Transfer learning for heterogeneous one-class collaborative filtering. IEEE Intell. Syst. 31(4), 43–49 (2016)CrossRefGoogle Scholar
  35. 35.
    Paquet, U., Koenigstein, N.: One-class collaborative filtering with random graphs. In: WWW, pp. 999–1008 (2013)Google Scholar
  36. 36.
    Park, S.T., Chu, W.: Pairwise preference regression for cold-start recommendation. In: Proceedings of the third ACM conference on Recommender systems, pp. 21–28 (2009)Google Scholar
  37. 37.
    Popescu-Belis, A., Pappas, N.: Sentiment analysis of user comments for one-class collaborative filtering over ted talks. In: 36Th ACM SIGIR conference on research and development in information retrieval, EPFL-CONF-192567. ACM (2013)Google Scholar
  38. 38.
    Rendle, S., Freudenthaler, C.: Improving pairwise learning for item recommendation from implicit feedback. In: WSDM, pp. 273–282 (2014)Google Scholar
  39. 39.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: bayesian personalized ranking from implicit feedback. In: UAI, pp. 452–461 (2009)Google Scholar
  40. 40.
    Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: SIGIR, pp. 253–260. ACM (2002)Google Scholar
  41. 41.
    Shen, Y., Jin, R.: Learning personal + social latent factor model for social recommendation. In: KDD, pp. 1303–1311 (2012)Google Scholar
  42. 42.
    Sindhwani, V., Bucak, S.S., Hu, J., Mojsilovic, A.: One-class matrix completion with low-density factorizations. In: ICDM, pp. 1055–1060 (2010)Google Scholar
  43. 43.
    Sun, M., Li, F., Lee, J., Zhou, K., Lebanon, G., Zha, H.: Learning multiple-question decision trees for cold-start recommendation. In: WSDM, pp. 445–454. ACM (2013)Google Scholar
  44. 44.
    Tang, J., Gao, H., Liu, H.: MTrust: discerning multi-faceted trust in a connected world. In: WSDM, pp. 93–102. ACM (2012)Google Scholar
  45. 45.
    Tang, J., Hu, X., Gao, H., Liu, H.: Exploiting local and global social context for recommendation. In: IJCAI, pp. 2712–2718 (2013)Google Scholar
  46. 46.
    Tang, J., Liu, H., Gao, H., Das Sarmas, A.: Etrust: understanding trust evolution in an online world. In: KDD, pp. 253–261. ACM (2012)Google Scholar
  47. 47.
    Wang, B., Ester, M., Bu, J., Zhu, Y., Guan, Z., Cai, D.: Which to view: personalized prioritization for broadcast emails. In: WWW, pp. 1181–1190 (2016)Google Scholar
  48. 48.
    Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: KDD, pp. 448–456. ACM (2011)Google Scholar
  49. 49.
    Xu, J., Yao, Y., Tong, H., Tao, X., Lu, J.: Ice-Breaking: mitigating cold-start recommendation problem by rating comparison. In: IJCAI, pp. 3981–3987 (2015)Google Scholar
  50. 50.
    Xu, J., Yao, Y., Tong, H., Tao, X., Lu, J.: Hoorays: high-order optimization of rating distance for recommender systems. In: KDD, pp. 525–534. ACM (2017)Google Scholar
  51. 51.
    Xu, J., Yao, Y., Tong, H., Tao, X., Lu, J.: Rapare: a generic strategy for cold-start rating prediction problem. IEEE Trans. Knowl. Data Eng. 29(6), 1296–1309 (2017)CrossRefGoogle Scholar
  52. 52.
    Yan, G., Yao, Y., Xu, F., Lu, J.: Rit: enhancing recommendation with inferred trust. In: PAKDD, pp. 756–767. Springer (2015)Google Scholar
  53. 53.
    Yang, S.H., Long, B., Smola, A., Sadagopan, N., Zheng, Z., Zha, H.: Like like alike: joint friendship and interest propagation in social networks. In: WWW, pp. 537–546. ACM (2011)Google Scholar
  54. 54.
    Yao, Y., Tong, H., Yan, G., Xu, F., Zhang, X., Szymanski, B.K., Lu, J.: Dual-regularized one-class collaborative filtering. In: CIKM, pp. 759–768. ACM (2014)Google Scholar
  55. 55.
    Yao, Y., Zhao, W.X., Wang, Y., Tong, H., Xu, F., Lu, J.: Version-aware rating prediction for mobile app recommendation. ACM Trans. Inf. Syst. 35(4), 38 (2017)CrossRefGoogle Scholar
  56. 56.
    Yu, H.F., Huang, H.Y., Dhillon, I.S., Lin, C.J.: A unified algorithm for one-cass structured matrix factorization with side information. In: AAAI, pp. 2845–2851 (2017)Google Scholar
  57. 57.
    Zhang, M., Tang, J., Zhang, X., Xue, X.: Addressing cold start in recommender systems: a semi-supervised co-training algorithm. In: SIGIR (2014)Google Scholar
  58. 58.
    Zhao, W.X., Li, S., He, Y., Chang, E.Y., Wen, J.R., Li, X.: Connecting social media to e-commerce: Cold-start product recommendation using microblogging information. IEEE Trans. Knowl. Data Eng. 28(5), 1147–1159 (2016)CrossRefGoogle Scholar
  59. 59.
    Zhao, X.W., Guo, Y., He, Y., Jiang, H., Wu, Y., Li, X.: We know what you want to buy: a demographic-based system for product recommendation on microblogs. In: KDD, pp. 1935–1944. ACM (2014)Google Scholar
  60. 60.
    Zheng, X., Ding, H., Mamitsuka, H., Zhu, S.: Collaborative matrix factorization with multiple similarities for predicting drug-target interactions. In: KDD, pp. 1025–1033 (2013)Google Scholar
  61. 61.
    Zhou, K., Yang, S.H., Zha, H.: Functional matrix factorizations for cold-start recommendation. In: SIGIR, pp. 315–324 (2011)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory for Novel Software TechnologyNanjingChina
  2. 2.Arizona State UniversityTempeUSA
  3. 3.Pennsylvania State UniversityState CollegeUSA
  4. 4.Rensselaer Polytechnic InstituteTroyUSA

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