Silence is also evidence: Interpreting dwell time for recommendation from psychological perspective

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn Part F128815, Issue , 2013, Pages 989-997

  Yin, Peifeng ^a   Luo, Ping ^b   Lee, Wang Chien ^a   Wang, Min ^c  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b HEWLETT PACKARD LABORATORIES   (United States)

^c GOOGLE INC   (United States)

Author keywords

Dwell time; Psychological; Recommendation

Indexed keywords

DATA MINING; SOCIAL NETWORKING (ONLINE);

DWELL TIME; EXPERIMENTAL EVALUATION; PSYCHOLOGICAL; RECOMMENDATION; RECOMMENDATION METHODS; RECOMMENDATION PERFORMANCE; SOCIAL MEDIA DATUM; USER EXPECTATIONS;

DECISION MAKING;

EID: 84970893538     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2487575.24876663     Document Type: Conference Paper

References (33)

1. D. Agarwal and B.-C. Chen. flda: matrix factorization through latent dirichlet allocation. In WSDM, pages 91-100, 2010.
2. E. Agichtein, E. Brill, and S. Dumais. Improving web search ranking by incorporating user behavior information. In SIGIR, pages 19-26, 2006.
3. H. J. Ahn. A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf. Sci., 178(1):37-51, 2008.
4. M. Bilenko and R. W. White. Mining the search trails of surfing crowds: identifying relevant websites from user activity. In WWW, pages 51-60, 2008.
5. J. Bobadilla, F. Ortega, A. Hernando, and J. Bernal. A collaborative filtering approach to mitigate the new user cold start problem. Knowledge-Based Systems, 26(1):225-238, 2012.
6. O. Chapelle and Y. Zhang. A dynamic bayesian network click model for web search ranking. In WWW, pages 1-10, 2009.
7. P. Cremonesi and R. Turrin. Analysis of cold-start recommendations in iptv systems. In RecSys, pages 233-236, 2009.
8. A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, 39(1):1-38, 1977.
9. A. Einstein. On the movement of small particles suspended in a stationary liquid demanded by the molecular-kinetic theory of heat. Annalen der Physik, 17:549-560, 1905.
10. Z. Gantner, L. Drumond, C. Freudenthaler, S. Rendle, and L. Schmidt-Thieme. Learning attribute-To-feature mappings for cold-start recommendations. In ICDM, pages 176-185, 2010.
11. Z. Gantner, S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. MyMediaLite: A free recommender system library. In RecSys, pages 305-308, 2011.
12. J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In SIGIR, pages 230-237, 1999.
13. Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In ICDM.
14. G. Jawaheer, M. Szomszor, and P. Kostkova. Comparison of implicit and explicit feedback from an online music recommendation service. In HetRec, pages 47-51, 2010.
15. D. Kelly and N. J. Belkin. Display time as implicit feedback: understanding task effects. In SIGIR, pages 377-384, 2004.
16. Y. Koren. Factorization meets the neighborhood: A multifaceted collaborative filtering model. In KDD, pages 426-434, 2008.
17. Y. Koren, R. M. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, 42(8):30-37, 2009.
18. C. Liu, R. W. White, and S. Dumais. Understanding web browsing behaviors through weibull analysis of dwell time. In SIGIR, pages 379-386, 2010.
19. N. N. Liu, E. W. Xiang, M. Zhao, and Q. Yang. Unifying explicit and implicit feedback for collaborative filtering. In CIKM, pages 1445-1448, 2010.
20. Y. Liu, B. Gao, T.-Y. Liu, Y. Zhang, Z. Ma, S. He, and H. Li. Browserank: letting web users vote for page importance. In SIGIR, pages 451-458, 2008.
21. H. Ma, I. King, and M. R. Lyu. Learning to recommend with social trust ensemble. In SIGIR, pages 203-210, 2009.
22. H. Ma, H. Yang, M. R. Lyu, and I. King. Sorec: social recommendation using probabilistic matrix factorization. In CIKM, pages 931-940, 2008.
23. P. Melville, R. J. Mooney, and R. Nagarajan. Content-boosted collaborative filtering for improved recommendations. In AAAI/IAAI, pages 187-192, 2002.
24. Y. Moshfeghi, B. Piwowarski, and J. M. Jose. Handling data sparsity in collaborative filtering using emotion and semantic based features. In SIGIR, pages 625-634, 2011.
25. E. R. Nunez-Valdez, J. M. C. Lovelle, O. S. Martinez, V. Garcia-Diaz, P. O. de Pablos, and C. E. M. Marin. Implicit feedback techniques on recommender systems applied to electronic books. Computers in Human Behavior, 28(4), 2012.
26. M. Papagelis, D. Plexousakis, and T. Kutsuras. Alleviating the sparsity problem of collaborative filtering using trust inferences. In iTrust, pages 224-239, 2005.
27. R. Ratcliff and G. McKoon. The diffusion decision model: Theory and data for two-choice decision tasks. Neural Computation, 20(4):873-922, 2008.
28. A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock. Methods and metrics for cold-start recommendations. In SIGIR, 2002.
29. E.-J. Wagenmakers. Methodological and empirical developments for the ratcliff diffusion model of response times and accuracy. European Journal of Cognitive Psychology, 21(5):641-671, 2009.
30. R. W. White and D. Kelly. A study on the effects of personalization and task information on implicit feedback performance. In CIKM.
31. Y. Xin and H. Steck. Multi-value probabilistic matrix factorization for ip-Tv recommendations. In RecSys, pages 221-228, 2011.
32. 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.
33. T. Zhou, Z. Kuscsik, J.-G. Liu, M. Medo, J. R. Wakeling, and Y.-C. Zhang. Solving the apparent diversity-Accuracy dilemma of recommender systems. PNAS, 2010.