-
2
-
-
84858024638
-
Learning to rank using an ensemble of lambda-gradient models
-
to appear in
-
C. J. Burges, K. M. Svore, P. N. Benett, A. Pastusiak, and Q. Wu. Learning to rank using an ensemble of lambda-gradient models. to appear in Special Edition of JMLR: Proceedings of the Yahoo! Learning to Rank Challenge, 14:25-35, 2011.
-
(2011)
Special Edition of JMLR: Proceedings of the Yahoo! Learning to Rank Challenge
, vol.14
, pp. 25-35
-
-
Burges, C.J.1
Svore, K.M.2
Benett, P.N.3
Pastusiak, A.4
Wu, Q.5
-
5
-
-
31844446958
-
Learning to rank using gradient descent
-
C. J. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In Proceedings of the International Conference on Machine learning, pages 89-96, 2005.
-
(2005)
Proceedings of the International Conference on Machine Learning
, pp. 89-96
-
-
Burges, C.J.C.1
Shaked, T.2
Renshaw, E.3
Lazier, A.4
Deeds, M.5
Hamilton, N.6
Hullender, G.7
-
6
-
-
34547987951
-
Learning to rank: From pairwise approach to listwise approach
-
Z. Cao, T. Qin, T.-Y. Liu, M.-F. Tsai, and H. Li. Learning to rank: from pairwise approach to listwise approach. In Proceedings of the International Conference on Machine learning, pages 129-136, 2007.
-
(2007)
Proceedings of the International Conference on Machine Learning
, pp. 129-136
-
-
Cao, Z.1
Qin, T.2
Liu, T.-Y.3
Tsai, M.-F.4
Li, H.5
-
7
-
-
65449139973
-
Structured learning for non-smooth ranking losses
-
S. Chakrabarti, R. Khanna, U. Sawant, and C. Bhattacharyya. Structured learning for non-smooth ranking losses. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 88-96, 2008.
-
(2008)
Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining
, pp. 88-96
-
-
Chakrabarti, S.1
Khanna, R.2
Sawant, U.3
Bhattacharyya, C.4
-
11
-
-
0035470889
-
Greedy function approximation: A gradient boosting machine
-
J. H. Friedman. Greedy function approximation: A gradient boosting machine. In Annals of Statistics, pages 29(5):1189-1232, 2001.
-
(2001)
Annals of Statistics
, vol.PAGES 29
, Issue.5
, pp. 1189-1232
-
-
Friedman, J.H.1
-
12
-
-
74549137182
-
Click chain model in web search
-
Association for Computing Machinery, Inc.
-
F. Guo, C. Liu, A. Kannan, T. Minka, M. Taylor, Y.-M. Wang, and C. Faloutsos. Click chain model in web search. In Proceedings of the 18th International World Wide Web Conference. Association for Computing Machinery, Inc., 2009.
-
(2009)
Proceedings of the 18th International World Wide Web Conference
-
-
Guo, F.1
Liu, C.2
Kannan, A.3
Minka, T.4
Taylor, M.5
Wang, Y.-M.6
Faloutsos, C.7
-
15
-
-
34247882698
-
Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search
-
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 Transactions on Information Science, 2007.
-
(2007)
ACM Transactions on Information Science
-
-
Joachims, T.1
Granka, L.2
Pan, B.3
Hembrooke, H.4
Radlinski, F.5
Gay, G.6
-
17
-
-
42549161120
-
Softrank: Optimizing non-smooth rank metrics
-
M. Taylor, J. Guiver, S. Robertson, and T. Minka. Softrank: optimizing non-smooth rank metrics. In Proceedings of the International Conference on Web Search and Web Data Mining, pages 77-86, 2008.
-
(2008)
Proceedings of the International Conference on Web Search and Web Data Mining
, pp. 77-86
-
-
Taylor, M.1
Guiver, J.2
Robertson, S.3
Minka, T.4
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