Towards a dynamic top-N recommendation framework

ABSTRACT

Real world large-scale recommender systems are always dynamic: new users and items continuously enter the system, and the status of old ones (e.g., users' preference and items' popularity) evolve over time. In order to handle such dynamics, we propose a recommendation framework consisting of an online component and an offline component, where the newly arrived items are processed by the online component such that users are able to get suggestions for fresh information, and the influence of longstanding items is captured by the offline component. Based on individual users' rating behavior, recommendations from the two components are combined to provide top-N recommendation. We formulate recommendation problem as a ranking problem where learning to rank is applied to extend upon matrix factorization to optimize item rankings by minimizing a pairwise loss function. Furthermore, to better model interactions between users and items, Latent Dirichlet Allocation is incorporated to fuse rating information and textual information. Real data based experiments demonstrate that our approach outperforms the state-of-the-art models by at least 61.21% and 50.27% in terms of mean average precision (MAP) and normalized discounted cumulative gain (NDCG) respectively.

References

1. D. Agarwal and B.-C. Chen. flda: matrix factorization through latent dirichlet allocation. In Proceedings of the third ACM WSDM, 2010.
2. D. Agarwal, B.-C. Chen, and P. Elango. Fast online learning through offline initialization for time-sensitive recommendation. In Proceedings of the 16th ACM SIGKDD, 2010.
3. M. Aly, S. Pandey, V. Josifovski, and K. Punera. Towards a robust modeling of temporal interest change patterns for behavioral targeting. In Proceedings of the 22nd WWW, 2013.
4. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993--1022, 2003.
5. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In Proceedings of the 22nd ICML, 2005.
6. I. Cantador, P. Brusilovsky, and T. Kuflik. 2nd workshop on information heterogeneity and fusion in recommender systems (hetrec). In Proceedings of the 5th ACM RecSys, 2011.
7. K. Chen, T. Chen, G. Zheng, O. Jin, E. Yao, and Y. Yu. Collaborative personalized tweet recommendation. In Proceedings of the 35th ACM SIGIR, 2012.
8. J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, Q. V. Le, M. Z. Mao, M. Ranzato, A. W. Senior, P. A. Tucker, K. Yang, and A. Y. Ng. Large scale distributed deep networks. In Proceedings of the 25th NIPS, 2012.
9. E. Diaz-Aviles, L. Drumond, L. Schmidt-Thieme, and W. Nejdl. Real-time top-n recommendation in social streams. In Proceedings of the 6th ACM RecSys, 2012.
10. E. Diaz-Aviles, L. Drumond, L. Schmidt-Thieme, and W. Nejdl. Real-time top-n recommendation in social streams. In Proceedings of the 6th ACM RecSys, 2012.
11. R. Gemulla, E. Nijkamp, P. J. Haas, and Y. Sismanis. Large-scale matrix factorization with distributed stochastic gradient descent. In Proceedings of the 17th ACM SIGKDD, 2011.
12. S. Kabbur, X. Ning, and G. Karypis. Fism: factored item similarity models for top-n recommender systems. In Proceedings of the 19th ACM SIGKDD, 2013.
13. Y. Koren. Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD, 2009.
14. N. Lathia, S. Hailes, and L. Capra. Temporal collaborative filtering with adaptive neighbourhoods. In Proceedings of the 32nd ACM SIGIR, 2009.
15. N. N. Liu, M. Zhao, E. Xiang, and Q. Yang. Online evolutionary collaborative filtering. In Proceedings of the 4th ACM RecSys, 2010.
16. X. Liu and K. Aberer. Soco: a social network aided context-aware recommender system. In Proceedings of the 22nd WWW, 2013.
17. J. McAuley and J. Leskovec. Hidden factors and hidden topics: Understanding rating dimensions with review text. In Proceedings of the 7th ACM RecSys, 2013.
18. S. Purushotham and Y. Liu. Collaborative topic regression with social matrix factorization for recommendation systems. In Proceedings of the 29th ICML, 2012.
19. Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, and A. Hanjalic. xclimf: Optimizing expected reciprocal rank for data with multiple levels of relevance. In Proceedings of the 7th ACM RecSys, 2013.
20. Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, A. Hanjalic, and N. Oliver. Tfmap: optimizing map for top-n context-aware recommendation. In Proceedings of the 35th ACM SIGIR, 2012.
21. Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, N. Oliver, and A. Hanjalic. Climf: learning to maximize reciprocal rank with collaborative less-is-more filtering. In Proceedings of the 6th ACM RecSys, 2012.
22. C. Wang and D. M. Blei. Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD, 2011.
23. F. Wang, W. Pan, and L. Chen. Recommendation for new users with partial preferences by integrating product reviews with static specifications. In Proceedings of 21st UMAP, 2011.
24. M. Weimer, A. Karatzoglou, Q. Le, and A. Smola. Cofirank - maximum margin matrix factorization for collaborative ranking. In Proceedings of the 21st NIPS, 2007.
25. L. Xiong, X. Chen, T.-K. Huang, J. Schneider, and J. G. Carbonell. Temporal collaborative filtering with bayesian probabilistic tensor factorization. In Proceedings of SDM, 2010.
26. E. Zhong, W. Fan, J. Wang, L. Xiao, and Y. Li. Comsoc: adaptive transfer of user behaviors over composite social network. In Proceedings of the 18th ACM SIGKDD, 2012.

Index Terms

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Document filtering

      • Information extraction

  • Information systems applications