Modeling contextual agreement in preferences

ABSTRACT

Personalization, or customizing the experience of each individual user, is seen as a useful way to navigate the huge variety of choices on the Web today. A key tenet of personalization is the capacity to model user preferences. The paradigm has shifted from that of individual preferences, whereby we look at a user's past activities alone, to that of shared preferences, whereby we model the similarities in preferences between pairs of users (e.g., friends, people with similar interests). However, shared preferences are still too granular, because it assumes that a pair of users would share preferences across all items. We therefore postulate the need to pay attention to "context", which refers to the specific item on which the preferences between two users are to be estimated. In this paper, we propose a generative model for contextual agreement in preferences. For every triplet consisting of two users and an item, the model estimates both the prior probability of agreement between the two users, as well as the posterior probability of agreement with respect to the item at hand. The model parameters are estimated from ratings data. To extend the model to unseen ratings, we further propose several matrix factorization techniques focused on predicting agreement, rather than ratings. Experiments on real-life data show that our model yields context-specific similarity values that perform better on a prediction task than models relying on shared preferences.

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. TKDE, 17(6), 2005.
2. A. Ahmed, B. Kanagal, S. Pandey, V. Josifovski, L. G. Pueyo, and J. Yuan. Latent factor models with additive and hierarchically-smoothed user preferences. In WSDM, 2013.
3. C. M. Bishop and N. M. Nasrabadi. Pattern Recognition and Machine Learning. Springer, 2006.
4. S. P. Boyd and L. Vandenberghe. Convex optimization. Cambridge University Press, 2004.
5. J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In UAI, 1998.
6. H. Fang, Y. Baoy, and J. Zhang. Misleading opinions provided by advisors: Dishonesty or subjectivity. In IJCAI, 2013.
7. T. J. Hastie, R. J. Tibshirani, and J. H. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2011.
8. T. Hofmann. Collaborative filtering via gaussian probabilistic latent semantic analysis. In SIGIR, 2003.
9. T. Hofmann. Latent semantic models for collaborative filtering. TOIS, 22(1), 2004.
10. L. Hu, J. Cao, G. Xu, L. Cao, Z. Gu, and C. Zhu. Personalized recommendation via cross-domain triadic factorization. In WWW, 2013.
11. D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich. Recommender Systems: An Introduction. Cambridge University Press, 2010.
12. R. Jin, J. Y. Chai, and L. Si. An automatic weighting scheme for collaborative filtering. In SIGIR, 2004.
13. B. Kanagal, A. Ahmed, S. Pandey, V. Josifovski, J. Yuan, and L. Garcia-Pueyo. Supercharging recommender systems using taxonomies for learning user purchase behavior. PVLDB, 5(10), 2012.
14. N. Koenigstein, G. Dror, and Y. Koren. Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy. In RecSys, 2011.
15. Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8), 2009.
16. Y. Koren and J. Sill. OrdRec: An ordinal model for predicting personalized item rating distributions. In RecSys, 2011.
17. N. D. Lawrence and R. Urtasun. Non-linear matrix factorization with gaussian processes. In ICML, 2009.
18. D. D. Lee and H. S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 1999.
19. G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1), 2003.
20. X. Liu and K. Aberer. SoCo: a social network aided context-aware recommender system. In WWW, 2013.
21. P. Lops, M. de Gemmis, and G. Semeraro. Content-based recommender systems: State of the art and trends. In Recommender Systems Handbook, pages 73--105. Springer, 2011.
22. H. Ma, I. King, and M. R. Lyu. Learning to recommend with social trust ensemble. In SIGIR, 2009.
23. H. Ma, H. Yang, M. R. Lyu, and I. King. SoRec: Social recommendation using probabilistic matrix factorization. In CIKM, 2008.
24. H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King. Recommender systems with social regularization. In WSDM, 2011.
25. L. W. Mackey, D. Weiss, and M. I. Jordan. Mixed membership matrix factorization. In ICML, 2010.
26. L. B. Marinho, A. Nanopoulos, L. Schmidt-Thieme, R. Jaschke, A. Hotho, G. Stumme, and P. Symeonidis. Social tagging recommender systems. In Recommender Systems Handbook, pages 615--644. Springer, 2011.
27. A. K. Menon, K.-P. Chitrapura, S. Garg, D. Agarwal, and N. Kota. Response prediction using collaborative filtering with hierarchies and side-information. In KDD, 2011.
28. R. Missaoui, P. Valtchev, C. Djeraba, and M. Adda. Toward recommendation based on ontology-powered web-usage mining. IEEE Internet Computing, 11(4), 2007.
29. A. Mnih and R. Salakhutdinov. Probabilistic matrix factorization. In NIPS, 2007.
30. W. Pan and L. Chen. GBPR: Group preference based bayesian personalized ranking for one-class collaborative filtering. In IJCAI, 2013.
31. M. J. Pazzani and D. Billsus. Content-based recommendation systems. In The Adaptive Web, pages 325--341. Springer, 2007.
32. S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. BPR: Bayesian personalized ranking from implicit feedback. In UAI, 2009.
33. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. GroupLens: an open architecture for collaborative filtering of netnews. In CSCW, 1994.
34. R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using markov chain monte carlo. In ICML, 2008.
35. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW, 2001.
36. H. Shan, J. Kattge, P. B. Reich, A. Banerjee, F. Schrodt, and M. Reichstein. Gap filling in the plant kingdom trait prediction using hierarchical probabilistic matrix factorization. In ICML, 2012.
37. Y. Shen and R. Jin. Learning personal
38. social latent factor model for social recommendation. In KDD, 2012.
39. A. P. Singh and G. J. Gordon. Relational learning via collective matrix factorization. In KDD, 2008.
40. N. Srebro, J. Rennie, and T. S. Jaakkola. Maximum-margin matrix factorization. In NIPS, 2004.
41. J. Wang, Y. Zhang, C. Posse, and A. Bhasin. Is it time for a career switch? In WWW, 2013.
42. E. Zhong, W. Fan, and Q. Yang. Contextual collaborative filtering via hierarchical matrix factorization. In SDM, 2012.

Index Terms

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Document filtering

      • Information extraction

  • Information systems applications

    • Data mining