COM: a generative model for group recommendation

ABSTRACT

With the rapid development of online social networks, a growing number of people are willing to share their group activities, e.g. having dinners with colleagues, and watching movies with spouses. This motivates the studies on group recommendation, which aims to recommend items for a group of users. Group recommendation is a challenging problem because different group members have different preferences, and how to make a trade-off among their preferences for recommendation is still an open problem. In this paper, we propose a probabilistic model named COM (COnsensus Model) to model the generative process of group activities, and make group recommendations. Intuitively, users in a group may have different influences, and those who are expert in topics relevant to the group are usually more influential. In addition, users in a group may behave differently as group members from as individuals. COM is designed based on these intuitions, and is able to incorporate both users' selection history and personal considerations of content factors. When making recommendations, COM estimates the preference of a group to an item by aggregating the preferences of the group members with different weights. We conduct extensive experiments on four datasets, and the results show that the proposed model is effective in making group recommendations, and outperforms baseline methods significantly.

References

1. G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng., 17(6):734--749, 2005.
2. D. Agarwal and B.-C. Chen. flda: matrix factorization through latent dirichlet allocation. In WSDM, pages 91--100, 2010.
3. S. Amer-Yahia, S. B. Roy, A. Chawla, G. Das, and C. Yu. Group recommendation: Semantics and efficiency. PVLDB, 2(1):754--765, 2009.
4. L. Baltrunas, T. Makcinskas, and F. Ricci. Group recommendations with rank aggregation and collaborative filtering. In RecSys, pages 119--126, 2010.
5. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993--1022, 2003.
6. L. A. M. C. Carvalho and H. T. Macedo. Users' satisfaction in recommendation systems for groups: an approach based on noncooperative games. In WWW (Companion Volume), pages 951--958, 2013.
7. A. Crossen, J. Budzik, and K. J. Hammond. Flytrap: intelligent group music recommendation. In IUI, pages 184--185, 2002.
8. J. Gorla, N. Lathia, S. Robertson, and J. Wang. Probabilistic group recommendation via information matching. In WWW, pages 495--504, 2013.
9. T. L. Griffiths and M. Steyvers. Finding scientific topics. Proceedings of the National academy of Sciences of the United States of America, 101(Suppl 1):5228--5235, 2004.
10. A. Jameson and B. Smyth. Recommendation to groups. In The adaptive web, pages 596--627. Springer, 2007.
11. Y. Koren, R. M. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, 42(8):30--37, 2009.
12. X. Liu, Q. He, Y. Tian, W.-C. Lee, J. McPherson, and J. Han. Event-based social networks: linking the online and offline social worlds. In KDD, pages 1032--1040, 2012.
13. X. Liu, Y. Tian, M. Ye, and W.-C. Lee. Exploring personal impact for group recommendation. In CIKM, pages 674--683, 2012.
14. J. F. McCarthy. Pocket restaurant finder: A situated recommender systems for groups. In Proceeding of Workshop on Mobile Ad-Hoc Communication at the 2002 ACM Conference on Human Factors in Computer Systems, 2002.
15. J. F. McCarthy and T. D. Anagnost. Musicfx: An arbiter of group preferences for computer aupported collaborative workouts. In CSCW, pages 363--372, 1998.
16. K. McCarthy, M. Salamó, L. Coyle, L. McGinty, B. Smyth, and P. Nixon. Cats: A synchronous approach to collaborative group recommendation. In FLAIRS Conference, volume 2006, pages 86--91, 2006.
17. M. R. McLaughlin and J. L. Herlocker. A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In SIGIR, pages 329--336, 2004.
18. M. O'Connor, D. Cosley, J. A. Konstan, and J. Riedl. Polylens: A recommender system for groups of user. In ECSCW, pages 199--218, 2001.
19. M. O'Connor and J. Herlocker. Clustering items for collaborative filtering. In Proceedings of the ACM SIGIR workshop on recommender systems, volume 128. UC Berkeley, 1999.
20. S. Pizzutilo, B. De Carolis, G. Cozzolongo, and F. Ambruoso. Group modeling in a public space: methods, techniques, experiences. In Proceedings of the 5th WSEAS International Conference on Applied Informatics and Communications, pages 175--180, 2005.
21. B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW, pages 285--295, 2001.
22. S. Seko, T. Yagi, M. Motegi, and S. yo Muto. Group recommendation using feature space representing behavioral tendency and power balance among members. In RecSys, pages 101--108, 2011.
23. X. Su and T. M. Khoshgoftaar. A survey of collaborative filtering techniques. Adv. Artificial Intellegence, 2009, 2009.
24. L. H. Ungar and D. P. Foster. Clustering methods for collaborative filtering. In AAAI Workshop on Recommendation Systems, number 1, 1998.
25. C. Wang and D. M. Blei. Collaborative topic modeling for recommending scientific articles. In KDD, pages 448--456, 2011.
26. M. Ye, X. Liu, and W.-C. Lee. Exploring social influence for recommendation: a generative model approach. In SIGIR, pages 671--680, 2012.
27. M. Ye, P. Yin, W.-C. Lee, and D. L. Lee. Exploiting geographical influence for collaborative point-of-interest recommendation. In SIGIR, pages 325--334, 2011.
28. Z. Yu, X. Zhou, Y. Hao, and J. Gu. Tv program recommendation for multiple viewers based on user profile merging. User Model. User-Adapt. Interact., 16(1):63--82, 2006.
29. Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. Magnenat-Thalmann. Time-aware point-of-interest recommendation. In SIGIR, pages 363--372, 2013.
30. Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. Magnenat-Thalmann. Who, where, when and what: discover spatio-temporal topics for twitter users. In KDD, pages 605--613, 2013.
31. W. Zhang, J. Wang, and W. Feng. Combining latent factor model with location features for event-based group recommendation. In KDD, pages 910--918, 2013.

Index Terms

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Document filtering

      • Information extraction