CMAP: Effective fusion of quality and relevance for multi-criteria recommendation

Proceedings of the 4th ACM International Conference on Web Search and Data Mining, WSDM 2011

Volumn , Issue , 2011, Pages 455-464

  Xin, Xin ^a   Lyu, Michael R ^a   King, Irwin ^b  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b AT AND T LABS RESEARCH   (United States)

Author keywords

Collaborative filtering; Continuous time markov process; Recommender system

Indexed keywords

COLLABORATIVE FILTERING; COMBINATION METHOD; COMBINED METHOD; COMPUTATIONAL TIME; CONTINUOUS TIME; EXPERIMENTAL VERIFICATION; HIGH QUALITY; LINEAR COMPLEXITY; MULTI-CRITERIA; NATURAL INTEGRATION; QUALITATIVE AND QUANTITATIVE ANALYSIS; REAL APPLICATIONS; REGRESSION PROBLEM; RESEARCH ISSUES; UNIFIED MODEL;

ALGORITHMS; CONTINUOUS TIME SYSTEMS; DATA MINING; INTEGRATION; MARKOV PROCESSES; QUALITY CONTROL; WORLD WIDE WEB;

RECOMMENDER SYSTEMS;

EID: 79952379467     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1935826.1935895     Document Type: Conference Paper

References (41)

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 TKDE, 17 (6): 734-749, 2005. (Pubitemid 40860454)
2. W. Anderson. Continuous-time Markov chains: Applications-oriented Approach. Springer, 1991.
3. C. Basu, H. Hirsh, and W. Cohen. Recommendation as classification: Using social and content-based information in recommendation. In Proc. of the NCAI, pages 714-720, 1998.
4. J. Breese, D. Heckerman, C. Kadie, et al. Empirical analysis of predictive algorithms for collaborative filtering. In Proc. of UAI 98, pages 43-52, 1998.
5. R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12 (4): 331-370, 2002.
6. B. Croft, D. Metzler, and T. Strohman. Search Engines: Information Retrieval in Practice. Addison Wesley, 2009.
7. M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS), 22 (1): 143-177, 2004.
8. M. Dummett. The Borda count and agenda manipulation. Social Choice and Welfare, 15 (2): 289-296, 1998.
9. A. Elberse. Should you invest in the long tail? harvard business review, 86 (7/8): 88-96, 2008.
10. A. Elberse and E Oberholzer-Gee. Superstars and underdogs: an examination of the long tail phenomenon in video sales. MSI Reports, 45 (4):49-72, 2007.
11. D. Fleder and K. Hosanagar. Recommender systems and their impact on sales diversity. In Proc. of EC 2007, page 199. ACM, 2007.
12. D. Frank Hsu and I. Taksa. Comparing rank and score combination methods for data fusion in information retrieval. Information Retrieval, 8 (3): 449-480, 2005. (Pubitemid 40550482)
13. Z. Guan, J. Bu, Q. Mei, C. Chen, and C. Wang. Personalized tag recommendation using graph-based ranking on multi-type interrelated objects. In Proc. of SIGIR'09, pages 540-547, 2009.
14. I. Herlocker, J. Konstan, and J. Riedl. An algorithmic framework for performing collaborative filtering. Inv. Proc. of SIGIR 99, pages 230-237. ACM New York, NY, USA, 1999.
15. I. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22 (1): 5-53, 2004.
16. T. Hofmann. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems, 22 (1):89-115, 2004.
17. Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proc. of SIGKDD 08, pages 426-434. ACM, 2008.
18. M. Kurucz, A. Benczúr, T. Kiss, I. Nagy, A. Szabó, and B. Torma. Who Rated What: a combination of SVD, correlation and frequent sequence mining. In Proc. KDD Cup and Workshop, volume 23, pages 720-727, 2007.
19. G. Linden, B. Smith, and J. York. Amazon, com recommendations: Item-to-item collaborative filtering. IEEE Internet computing, 2003.
20. N. Liu and Q. Yang. EigenRank: a ranking-oriented approach to collaborative filtering. In Proc. of SIGIR 08, pages 83-90. ACM New York, NY, USA, 2008.
21. Y. Liu, B. Gao, T. Liu, Y. Zhang, Z. Ma, S. He, and H. Li. BrowseRank: letting web users vote for page importance. In Proc. of SIGIR 08, pages 451-458. ACM New York, NY, USA, 2008.
22. H. Ma, I. King, and M. Lyu. Effective missing data prediction for collaborative filtering. In Proc. of SIGIR 07, pages 39-46. ACM New York, NY, USA, 2007.
23. H. Ma, I. King, and M. Lyu. Learning to recommend with social trust ensemble. In Proc. of ACM SIGIR'09, pages 203-210. ACM, 2009.
24. J. Marden. Analyzing and modeling rank data. Chapman & Hall/CRC, 1995.
25. L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. 1998.
26. D. Pennock, E. Horvitz, S. Lawrence, and C. Giles. Collaborative filtering by personality diagnosis: A hybrid memory-and model-based approach. In Proceedings of UAI 00, pages 473-480. Stanford, California, 2000.
27. S. Rosset, C. Perlich, and Y Liu. Making the most of your data: KDD Cup 2007" How Many Ratings" winner's report. 2007.
28. R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. Advances in neural information processing systems, 20, 2008.
29. B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. In Proc. of WWW 01, pages 285-295. ACM New York, NY, USA, 2001.
30. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Analysis of recommendation algorithms for e-commerce. In Proc. of EC 00, pages 158-167, New York, NY, USA, 2000. ACM.
31. L. Si and R. Jin. Flexible mixture model for collaborative filtering. In Proc. of ICML'03. ACM, NY, USA, 2003.
32. W. Stewart. Numerical solution of Markov chains. CRC Press, 1991.
33. J. Sueiras, A. Salafranca, and J. Florez. A classical predictive modeling approach for Task "Who rated what?" of the KDD CUP 2007. Proceedings of the KDD Cup, page 34, 2007.
34. E. Vozalis and K. Margaritis. Analysis of recommender systems algorithms. In Proc. of HERCMA-2003, 2003.
35. J. Wang, A. De Vries, and M. Reinders. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proc. of SIGIR 2006, pages 501-508. ACM New York, NY, USA, 2006.
36. M. Weimer, A. Karatzoglou, Q. Le, and A. Smola. Maximum Margin Matrix Factorization for Collaborative Ranking. Advances in neural information processing systems, 2007.
37. X. Xin, I. King, H. Deng, and M. Lyu. A social recommendation framework based on multi-scale continuous conditional random fields. In Proc. of CIKM'09, pages 1247-1256. ACM, 2009.
38. G. Xue, C. Lin, Q. Yang, W. Xi, H. Zeng, Y. Yu, andZ. Chen. Scalable collaborative filtering using cluster-based smoothing. In Proc. of SIGIR 2005, pages 114-121. ACM New York, NY, USA, 2005.
39. K. Yu, S. Zhu, J. Lafferty, and Y Gong. Fast nonparametric matrix factorization for large-scale collaborative filtering. In Proc. of SIGlR'09, pages 211-218. ACM, NY, USA, 2009.
40. Y. Zhang and J. Koren. Efficient bayesian hierarchical user modeling for recommendation system. In Proc. of SIGIR 2007, pages 47-54. ACM, 2007.
41. S. Zhu, K. Yu, and Y Gong. Stochastic relational models for large-scale dyadic data using MCMC. Advances in Neural Information Processing Systems 21: 1993-2000, 2009.