Exploring personal impact for group recommendation

ABSTRACT

Group activities are essential ingredients of people's social life. The rapid growth of online social networking services has greatly boosted group activities by providing convenient platform for users to organize and participate in such activities. Therefore, recommender systems, as a critical component in social networking services, now face new challenges in supporting group activities. In this paper, we study the group recommendation problem, i.e., making recommendations to a group of people in social networking services. We analyze the decision making process in a group to propose a personal impact topic (PIT) model for group recommendations. The PIT model effectively identifies the group preference profile for a given group by considering the personal preferences and personal impacts of group members. Moreover, we further enhance the discovery of personal impact with social network information to obtain an extended personal impact topic (E-PIT) model. We have conducted comprehensive data analysis and evaluations on three real datasets. The results show that our proposed group recommendation techniques outperform baseline approaches.

References

1. G. Adomavicius, G. Adomavicius, and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE TKDE, 17(6):734--749, 2005.
2. S. Amer-Yahia, S. B. Roy, A. Chawlat, G. Das, and C. Yu. Group recommendation: semantics and effciency. PVLDB, 2:754--765, 2009.
3. L. Ardissono, A. Goy, G. Petrone, M. Segnan, and P. Torasso. Intrigue: personalized recommendation of tourist attractions for desktop and hand held devices. Applied Artificial Intelligence, 17(8):687--714, 2003.
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. J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In UAI, pages 43--52, 1998.
7. J. S. Breese, D. Heckerman, and C. M. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In UAI, pages 43--52, 1998.
8. A. Crossen, J. Budzik, and K. J. Hammond. Flytrap: intelligent group music recommendation. In IUI, pages 184--185, 2002.
9. D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. CACM, 35:61--70, 1992.
10. T. Hofmann. Latent semantic models for collaborative filtering. TOIS, 22(1):89--115, 2004.
11. A. Jameson and B. Smyth. The adaptive web. chapter Recommendation to groups, pages 596--627. 2007.
12. X. Liu, Q. He, Y. Tian, W.-C. Lee, J. McPherson, and J. Han. Event-based social networks: Linking the online and offine social worlds. In KDD, 2012.
13. J. Masthoff. Group modeling: Selecting a sequence of television items to suit a group of viewers. UMUAI, 14:37--85, 2004.
14. J. F. McCarthy and T. D. Anagnost. Musicfx: an arbiter of group preferences for computer supported collaborative workouts. In CSCW, pages 363--372, 1998.
15. K. McCarthy, M. Salamo, L. Coyle, L. McGinty, B. Smyth, and P. Nixon. Cats: A synchronous approach to collaborative group recommendation. In FLAIRS, pages 86--91, 2006.
16. R. J. Mooney and L. Roy. Content-based book recommending using learning for text categorization. In DL, pages 195--204, 2000.
17. M. O'Connor, D. Cosley, J. A. Konstan, and J. Riedl. Polylens: a recommender system for groups of users. In ECSCW, pages 199--218, 2001.
18. S. Pizzutilo, B. De Carolis, G. Cozzolongo, and F. Ambruoso. Group modeling in a public space: methods, techniques, experiences. In WSEAS, pages 175--180, 2005.
19. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: an open architecture for collaborative filtering of netnews. In CSCW, pages 175--186, 1994.
20. M. Rosen-Zvi, T. Griffiths, M. Steyvers, and P. Smyth. The author-topic model for authors and documents. In UAI, pages 487--494, 2004.
21. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW, pages 285--295, 2001.
22. X. Su and T. M. Khoshgoftaar. A survey of collaborative filtering techniques. Advance in Artificial Intelligence, pages 4:2--4:2, 2009.
23. L. Ungar, L. Ungar, D. Foster, E. Andre, S. Wars, F. S. Wars, D. S. Wars, and J. H. Whispers. Clustering methods for collaborative filtering. In AAAI Workshop on Recommendation Systems, 1998.
24. M. Ye, X. Liu, and W.-C. Lee. Exploring social influence for recommendation - a generative model approach. In SIGIR, 2012.
25. Z. Yu, X. Zhou, Y. Hao, and J. Gu. Tv program recommendation for multiple viewers based on user profile merging. UMUAI, 16:63--82, 2006.

Index Terms

• Applied computing

  • Law, social and behavioral sciences

    • Sociology

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Document filtering

      • Information extraction