Information Technology Reference
In-Depth Information
12. Donmez, R., Carbonell, J.G.: Optimizing estimated loss reduction for active sampling in rank
learning. In: Proceedings of the 25th International Conference on Machine Learning (ICML
2008), pp. 248-255 (2008)
13. Dupret, G., Piwowarski, B.: A user browsing model to predict search engine click data from
past observations. In: Proceedings of the 31st Annual International Conference on Research
and Development in Information Retrieval (SIGIR 2008), pp. 331-338 (2008)
14. Freund, Y., Iyer, R., Schapire, R., Singer, Y.: An efficient boosting algorithm for combining
preferences. Journal of Machine Learning Research
4
, 933-969 (2003)
15. Gao, J., Yuan, W., Li, X., Deng, K., Nie, J.Y.: Smoothing clickthrough data for web search
ranking. In: Proceedings of the 32st Annual International Conference on Research and Devel-
opment in Information Retrieval (SIGIR 2009), pp. 355-362 (2009)
16. Geng, X., Liu, T.Y., Qin, T., Cheng, X.Q., Li, H.: Selecting optimal subset for learning to rank.
Information Processing and Management (2011)
17. Geng, X.B., Liu, T.Y., Qin, T., Li, H.: Feature selection for ranking. In: Proceedings of the 30th
Annual International ACM SIGIR Conference on Research and Development in Information
Retrieval (SIGIR 2007), pp. 407-414 (2007)
18. Guo, F., Liu, C., Kannan, A., Minka, T., Taylor, M., Wang, Y.M., Faloutsos, C.: Click chain
model in web search. In: Proceedings of the 18th International Conference on World Wide
Web (WWW 2009), pp. 11-20 (2009)
19. Guo, F., Liu, C., Wang, Y.M.: Efficient multiple-click models in web search. In: Proceedings
of the 1st International Conference on Web Search and Web Data Mining (WSDM 2008),
pp. 124-131 (2009)
20. Herbrich, R., Obermayer, K., Graepel, T.: Large margin rank boundaries for ordinal regres-
sion. In: Advances in Large Margin Classifiers, pp. 115-132 (2000)
21. Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the 8th
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD
2002), pp. 133-142 (2002)
22. Kendall, M.: Rank Correlation Methods. Oxford University Press, London (1990)
23. Li, P., Burges, C., Wu, Q.: McRank: Learning to rank using multiple classification and gradient
boosting. In: Advances in Neural Information Processing Systems 20 (NIPS 2007), pp. 845-
852 (2008)
24. Liu, C., Guo, F., Faloutsos, C.: Bbm: Bayesian browsing model for petabyte-scale data. In:
Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining (KDD 2009), pp. 537-546 (2009)
25. Liu, T.Y., Xu, J., Qin, T., Xiong, W.Y., Li, H.: LETOR: benchmark dataset for research on
learning to rank for information retrieval. In: SIGIR 2007 Workshop on Learning to Rank for
Information Retrieval (LR4IR 2007) (2007)
26. Pavlu, V.: Large scale ir evaluation. Ph.D. thesis, Northeastern University, College of Com-
puter and Information Science (2008)
27. Radlinski, F., Joachims, T.: Active exploration for learning rankings from clickthrough data.
In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining (KDD 2007) (2007)
28. Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the click-through
rate for new ads. In: Proceedings of the 16th International Conference on World Wide Web
(WWW 2007), pp. 521-530 (2007)
29. Wen, J.R., Nie, J.Y., Zhang, H.J.: Query clustering using user logs. ACM Transactions on
Information Systems
20
(1), 59-81 (2002)
30. Yi, J., Maghoul, F.: Query clustering using click-through graph. In: Proceedings of the 18th
International Conference on World Wide Web (WWW 2009), pp. 1055-1056 (2009)
31. Yilmaz, E., Aslam, J.A.: Estimating average precision with incomplete and imperfect judg-
ments. In: Proceedings of the Fifteenth ACM International Conference on Information and
Knowledge Management (CIKM 2006), pp. 102-111 (2006)
32. Yilmaz, E., Robertson, S.: Deep versus shallow judgments in learning to rank. In: Proceedings
of the 32st Annual International Conference on Research and Development in Information
Retrieval (SIGIR 2009), pp. 662-663 (2009)
