Click chain model in web search

WWW'09 - Proceedings of the 18th International World Wide Web Conference

Volumn , Issue , 2009, Pages 11-20

  Guo, Fan ^a   Liu, Chao ^b   Kannan, Anitha ^b   Minka, Tom ^c   Taylor, Michael ^c   Wang, Yi Min ^b   Faloutsos, Christos ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b MICROSOFT RESEARCH   (United States)

^c MICROSOFT RESEARCH   (United Kingdom)

Author keywords

Algorithms; Experimentation

Indexed keywords

BAYESIAN FRAMEWORKS; CHAIN MODELS; COMPUTATIONAL CHALLENGES; DATA SETS; EXPERIMENTAL STUDIES; EXPERIMENTATION; LOG LIKELIHOOD; WEB DOCUMENT; WEB SEARCHES;

ALGORITHMS; SEARCH ENGINES; WEBSITES;

INFORMATION RETRIEVAL;

EID: 74549137182     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1526709.1526712     Document Type: Conference Paper

References (17)

1. G. Aggarwal, J. Feldman, S. Muthukrishnan, and M. Pál. Sponsored search auctions with markovian users. In WINE '08, pages 621-628, 2008.
2. E. Agichtein, E. Brill, and S. Dumais. Improving web search ranking by incorporating user behavior information. In SIGIR '06, pages 19-26, 2006.
3. O. Chapelle and Y. Zhang. A dynamic bayesian network click model for web search ranking. In WWW '09, 2009.
4. N. Craswell, O. Zoeter, M. Taylor, and B. Ramsey. An experimental comparison of click position-bias models. In WSDM '08, pages 87-94, 2008.
5. G. E. Dupret and B. Piwowarski. A user browsing model to predict search engine click data from past observations. In SIGIR '08, pages 331-338, 2008.
6. F. Guo, L. Li, and C. Faloutsos. Tailoring click models to user goals. In WSDM '09 workshop on Web search click data, pages 88-92, 2009.
7. F. Guo, C. Liu, and Y.-M.Wang. Efficient multiple-click models in web search. In WSDM '09, pages 124-131, 2009
8. T. Joachims. Optimizing search engines using clickthrough data. In KDD '02, pages 133-142, 2002.
9. T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay. Accurately interpreting clickthrough data as implicit feedback. In SIGIR '05, pages 154-161, 2005.
10. T. Joachims, L. Granka, B. Pan, H. Hembrooke, F. Radlinski, and G. Gay. Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search. ACM Trans. Inf. Syst., 25(2):7, 2007.
11. D. Kempe and M. Mahdian. A cascade model for externalities in sponsored search. In WINE '08, pages 585-596, 2008.
12. U. Lee, Z. Liu, and J. Cho. Automatic identification of user goals in web search. In WWW '05, pages 391-400, 2005.
13. T. Minka. Expectation propagation for approximate bayesian inference. In UAI '01, pages 362-369, 2001.
14. B. Piwowarski, G. Dupret, and R. Jones. Mining user web search activity with layered bayesian networks or how to capture a click in its context. In WSDM '09, pages 162-171, 2009.
15. F. Radlinski and T. Joachims. Query chains: learning to rank from implicit feedback. In KDD '05, pages 239-248, 2005.
16. M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: estimating the click-through rate for new ads. In WWW '07, pages 521-530, 2007.
17. G.-R. Xue, H.-J. Zeng, Z. Chen, Y. Yu, W.-Y. Ma, W. Xi, and W. Fan. Optimizing web search using web click-through data. In CIKM '04, pages 118-126, 2004.