Preference-oriented social networks: Group recommendation and inference

RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems

Volumn , Issue , 2015, Pages 35-42

  Salehi Abari, Amirali ^a   Boutilier, Craig ^a,b  

^a UNIVERSITY OF TORONTO   (Canada)

^b GOOGLE INC   (United States)

Author keywords

Group recommendation; Preferences; Probabilistic inference; Probabilistic models; Social networks

Indexed keywords

SOCIAL NETWORKING (ONLINE);

GROUP RECOMMENDATIONS; INDIVIDUAL PREFERENCE; PARTIAL OBSERVATION; POLITICAL PARTIES; PREFERENCES; PROBABILISTIC INFERENCE; PROBABILISTIC MODELS; SOCIAL CONNECTION;

RECOMMENDER SYSTEMS;

EID: 84962915362     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2792838.2800190     Document Type: Conference Paper

References (42)

1. S. Amer-Yahia, S. B. Roy, A. Chawlat, G. Das, and C. Yu. Group recommendation: Semantics and eficiency. Proc. VLDB Endow., 2(1):754-765, 2009.
2. K. Arrow. Social Choice and Individual Values. 1951.
3. L. Baltrunas, T. Makcinskas, and F. Ricci. Group recommendations with rank aggregation and collaborative filtering. Proc. 4th ACM Conf. on Recommender Systems (RecSys-10), pp.119-126, 2010.
4. M. Barthélemy. Spatial networks. Physics Reports, 499(1):1-101, 2011.
5. S. Berkovsky and J. Freyne. Group-based recipe recommendations: Analysis of data aggregation strategies. Proc. 4th ACM Conf. on Recommender Systems (RecSys-10), pp.111-118, 2010.
6. M. Bilgic, L. Mihalkova, and L. Getoor. Active learning for networked data. Proc. 27th Intl. Conf. on Mach. Learn. (ICML-10), pp.79-86, 2010.
7. M. Boguñá and R. Pastor-Satorras. Class of correlated random networks with hidden variables. Physical Review E, 68:036112, Sep 2003.
8. C. Boutilier, I. Caragiannis, S. Haber, T. Lu, A. D. Procaccia, O. Sheffet. Optimal social choice functions: A utilitarian view. Proc. 13th ACM Conf. on Electronic Commerce (EC-12), pp.197-214, 2012.
9. L. M. Busse, P. Orbanz, J. M. Buhmann. Cluster analysis of heterogeneous rank data. Proc. 24th Intl. Conf. on Mach. Learn., pp.113-120, 2007.
10. N. A. Christakis and J. H. Fowler. The spread of obesity in a large social network over 32 years. New England J. Medicine, 357(4):370-379, 2007.
11. J.-P. Doignon, A. Pekec, and M. Regenwetter. The repeated insertion model for rankings: Missing link between two subset choice models. Psychometrika, 69(1):33-54, 2004.
12. M. Gartrell, X. Xing, Q. Lv, A. Beach, R. Han, S. Mishra, K. Seada. Enhancing group recommendation by incorporating social relationship interactions. Proc. 16th Intl. Conf. on Supp. Group Work, pp.97-106, 2010.
13. M. Gladwell. The Tipping Point: How Little Things can Make a Big Difference. Little Brown, 2000.
14. J. Golbeck and J. Hendler. Filmtrust: Movie recommendations using trust in web-based social networks. Proc. IEEE Consumer Communications and Networking Conf., Vol. 96, pp.282-286, 2006.
15. M. S. Granovetter. The strength of weak ties. American J. Sociology, 78(6):1360-1380, 1973.
16. P. D. Hoff, A. E. Raftery, and M. S. Handcock. Latent space approaches to social network analysis. J. American Statistical Assoc., 97(460):1090-1098, 2002.
17. M. Jackson. Social and Economic Networks. 2008.
18. M. Jamali, M. Ester. A matrix factorization technique with trust propagation for recommendation in social networks. Proc. 4th ACM Conf. on Recommender Systems (RecSys-10), pp.135-142, 2010.
19. M. Kim and J. Leskovec. Latent multi-group membership graph model. Proc. 29th Intl. Conf. on Machine Learning (ICML-12), pp.1719-1726, 2012.
20. I. Konstas, V. Stathopoulos, J. Jose. On social networks and collaborative recommendation. Proc. 32nd Intl. Conf. on R&D in Info. Retr. (SIGIR), pp.195-202, 2009.
21. K. Lewis, M. Gonzalez, and J. Kaufman. Social selection and peer inuence in an online social network. PNAS, 109(1):68-72, 2012.
22. T. Lu and C. Boutilier. Learning mallows models with pairwise preferences. Proc. 28th Intl. Conf. on Machine Learning (ICML-12), pp.145-152, 2011.
23. H. Ma, I. King, M. Lyu. Learning to recommend with social trust ensemble. Proc. 32nd Intl. Conf. on R&D in Info. Retr. (SIGIR), pp.203-210, 2009.
24. H. Ma, H. Yang, M. R. Lyu, and I. King. SoRec: Social recommendation using probabilistic matrix factorization. Proc. 17th ACM Conf. on Information and Knowledge Mgmt., pp.931-940, 2008.
25. J. I. Marden. Analyzing and Modeling Rank Data. Chapman and Hall, London, 1995.
26. P. Massa, P. Avesani. Controversial users demand local trust metrics: An experimental study on epinions.com community. Proc. 20th National Conf. on Artificial Intelligence (AAAI-05), pp.121-126, 2005.
27. J. MastHoff. Group modeling: Selecting a sequence of television items to suit a group of viewers. User Model. & User-Adapted Interact., 14(1):37-85, 2004.
28. J. MastHoff and A. Gatt. In pursuit of satisfaction and the prevention of embarrassment: Affective state in group recommender systems. User Model. & User-Adapted Interact., 16(3-4):281-319, 2006.
29. J. F. McCarthy, T. D. Anagnost. Musicfx: An arbiter of group preferences for computer supported collaborative workouts. Proc. 1998 ACM Conf. on Computer-supported Coop. Work, pp.363-372, 1998.
30. T. Murphy, D. Martin. Mixtures of distance-based models for ranking data. Comp. Stats. & Data Anal., 41(3):645-655, 2003.
31. M. Newman. Networks: An Intro. Oxford, 2010.
32. L. Quijano-Sanchez, J. Recio-Garcia, B. Diaz-Agudo, G. Jimenez-Diaz. Social factors in group recommender systems. ACM Trans. Intel. Sys. & Tech., 4(1):8, 2013.
33. A. Salehi-Abari and C. Boutilier. Ranking networks. NIPS-13 Workshop on Frontiers of Network Analysis: Methods, Models, and Applications, 2013.
34. A. Salehi-Abari and C. Boutilier. Empathetic social choice on social networks. Proc. 13th Intl. Conf. on Autonomous Agents and Multiagent Systems (AAMAS-14), pp.693-700, 2014.
35. S. Seko, T. Yagi, M. Motegi, and S. Muto. Group recommendation using feature space representing behavioral tendency and power balance among members. Proc. Fifth ACM Conf. on Recommender Systems (RecSys-11), pp.101-108, 2011.
36. A. K. Sen. Collective Choice and Social Welfare. North-Holland, 1970.
37. P. Sen, G. M. Namata, M. Bilgic, L. Getoor, B. Gallagher, and T. Eliassi-Rad. Collective classification in network data. AI Magazine, 29(3):93-106, 2008.
38. M. A. Serrano, D. Krioukov, and M. Boguñá. Self-similarity of complex networks and hidden metric spaces. Physical Review Letters, 100:078701, Feb 2008.
39. B. M. Waxman. Routing of multipoint connections. IEEE J. Selected Areas in Communications, 6(9):1617-1622, Dec. 1988.
40. Y. Xiwang, Y. Guo, Y. Liu, and H. Steck. A survey of collaborative filtering based social recommender systems. Computer Communications, 41:1-10, 2014.
41. X. Yang, H. Steck, Y. Liu. Circle-based recommendation in online social networks. Proc. 18th Intl. Conf. on Knowl. Disc. and Data Mining, pp.1267-1275, 2012.
42. Z. Yu, X. Zhou, Y. Hao, and J. Gu. TV program recommendation for multiple viewers based on user profile merging. User Model. & User-Adapted Interact., 16(1):63-82, 2006.