Learning click models via probit Bayesian inference

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2010, Pages 439-448

  Zhang, Yuchen ^a,b   Wang, Dong ^a,b   Wang, Gang ^b   Chen, Weizhu ^b   Zhang, Zhihua ^c   Hu, Botao ^a,b   Zhang, Li ^a  

^a TSINGHUA UNIVERSITY   (China)

^b MICROSOFT RESEARCH ASIA   (China)

^c ZHEJIANG UNIVERSITY   (China)

Author keywords

Click log analysis; Click model; Probit Bayesian inference

Indexed keywords

BAYESIAN FRAMEWORKS; BAYESIAN INFERENCE; BAYESIAN LEARNING; CLICK MODEL; CLICKTHROUGH DATA; GENERALIZATION ABILITY; INFERENCE METHODS; LOG ANALYSIS; LOG DATA; MODEL ASSUMPTIONS; PAGERANK SCORE; SEARCH BEHAVIOR; UNIFIED FRAMEWORK;

BEHAVIORAL RESEARCH; INFERENCE ENGINES; KNOWLEDGE MANAGEMENT;

BAYESIAN NETWORKS;

EID: 78651323980     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1871437.1871496     Document Type: Conference Paper

References (15)

1. E. Agichtein, E. Brill, S. Dumais, and R. Ragno. Learning user interaction models for predicting web search result preferences. In Proceedings of SIGIR2006, 2006.
2. R. Agrawal, A. Halverson, K. Kenthapadi, N. Mishra, and P. Tsaparas. Generating labels from clicks. In Proceedings of WSDM2009, 2009.
3. J. H. Albert and S. Chib. Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88(422):669-679, 1993.
4. C. M. Bishop. Pattern Recognition and Machine Learning. Springer, New York, 2006.
5. B. Carterette and R. Jones. Evaluating search engines by modeling the relationship between relevance and clicks. In Proceedings of NIPS20, 2008.
6. O. Chapelle and Y. Zhang. A dynamic bayesian network click model for web search ranking. In Proceedings of WWW2009, 2009.
7. N. Craswell, O. Zoeter, M. Taylor, and B. Ramsey. An experimental comparison of click position-bias models. In Proceedings of WSDM2008, 2008.
8. G. Dupret and B. Piwowarski. User browsing model to predict search engine click data from past observations. In Proceedings of SIGIR2008, 2008.
9. F. Guo, C. Liu, A. Kannan, T. Minka, M. Taylor, Y. Wang, and C. Faloutsos. Click chain model in web search. In Proceedings of WWW2009, 2009.
10. K. Jarvelin and J. Kekalainen. Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems 20(4), 422-446 (2002), 2002.
11. T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay. Accurately interpreting clickthrough data as implicit feedback. In Proceedings of SIGIR2005, 2005.
12. F. R. Kschischang, B. J. Frey, and H.-A. Loeliger. Factor graphs and the sum-product algorithm. IEEE Transactions on Information Throry, 1998.
13. T. Minka. A family of algorithms for approximate Bayesian inference. PhD thesis, Massachusetts Institute of Technology, 2001.
14. F. Radlinski and T. Joachims. Query chains: learning to rank from implicit feedback. In Proceedings of KDD2005, 2005.
15. M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: estimating the click-through rate for new ads. In Proceedings of WWW2007, 2007.