-
2
-
-
36448983903
-
A support vector method for optimizing average precision
-
Yisong Yue, Thomas Finley, Filip Radlinski, and Thorsten Joachims. A support vector method for optimizing average precision. In SIGIR 2007: Proceedings of the 30th annual Int. ACM SIGIR Conf. on Research and development in information retrieval, pages 271-278, 2007.
-
(2007)
SIGIR 2007: Proceedings of the 30th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval
, pp. 271-278
-
-
Yue, Y.1
Finley, T.2
Radlinski, F.3
Joachims, T.4
-
3
-
-
57649092193
-
Mcrank: Learning to rank using multiple classification and gradient boosting
-
Ping Li, Christopher Burges, and Qiang Wu. Mcrank: Learning to rank using multiple classification and gradient boosting. In Neural Information Processing System 2007.
-
(2007)
Neural Information Processing System
-
-
Li, P.1
Burges, C.2
Wu, Q.3
-
6
-
-
4644367942
-
An efficient boosting algorithm for combining preferences
-
Yoav Freund, Raj Iyer, Robert E. Schapire, and Yoram Singer. An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4:933-969, 2003.
-
(2003)
Journal of Machine Learning Research
, vol.4
, pp. 933-969
-
-
Freund, Y.1
Iyer, R.2
Schapire, R.E.3
Singer, Y.4
-
7
-
-
31844446958
-
Learning to rank using gradient descent
-
Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. Learning to rank using gradient descent. In International Conference on Machine Learning 2005, 2005.
-
(2005)
International Conference on Machine Learning 2005
-
-
Burges, C.1
Shaked, T.2
Renshaw, E.3
Lazier, A.4
Deeds, M.5
Hamilton, N.6
Hullender, G.7
-
8
-
-
33750338615
-
Adapting ranking svm to document retrieval
-
Yunbo Cao, Jun Xu, Tie-Yan Liu, Hang Li, Yalou Huang, and Hsiao-Wuen Hon. Adapting ranking svm to document retrieval. In SIGIR 2006: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 186-193, 2006.
-
(2006)
SIGIR 2006: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
, pp. 186-193
-
-
Cao, Y.1
Xu, J.2
Liu, T.-Y.3
Li, H.4
Huang, Y.5
Hon, H.-W.6
-
9
-
-
36448961557
-
Frank: A ranking method with fidelity loss
-
Ming Feng Tsai, Tie yan Liu, Tao Qin, Hsin hsi Chen, and Wei ying Ma. Frank: A ranking method with fidelity loss. In SIGIR 2007: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007.
-
(2007)
SIGIR 2007: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
-
-
Tsai, M.F.1
Liu, T.Y.2
Qin, T.3
Chen, H.H.4
Ma, W.Y.5
-
12
-
-
50149110023
-
-
Technical report
-
Tao Qin, Tie yan Liu, Ming feng Tsai, Xu dong Zhang, and Hang Li. Learning to search web pages with query-level loss functions. Technical report, 2006.
-
(2006)
Learning to Search Web Pages with Query-level Loss Functions
-
-
Qin, T.1
Liu, T.Y.2
Tsai, M.F.3
Zhang, X.D.4
Li, H.5
-
15
-
-
56449094442
-
Listwise approach to learning to rank: Theory and algorithm
-
Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li. Listwise approach to learning to rank: theory and algorithm. In Int. Conf. on Machine Learning 2008, pages 1192-1199, 2008.
-
(2008)
Int. Conf. on Machine Learning 2008
, pp. 1192-1199
-
-
Xia, F.1
Liu, T.-Y.2
Wang, J.3
Zhang, W.4
Li, H.5
-
17
-
-
71149095619
-
Boltzrank: Learning to maximize expected ranking gain
-
New York, NY, USA ACM
-
Maksims N. Volkovs and Richard S. Zemel. Boltzrank: learning to maximize expected ranking gain. In ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning, pages 1089-1096, New York, NY, USA, 2009. ACM.
-
(2009)
ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning
, pp. 1089-1096
-
-
Volkovs, M.N.1
Zemel, R.S.2
-
21
-
-
72449180896
-
Robust sparse rank learning for non-smooth ranking measures
-
New York, NY, USA ACM
-
Zhengya Sun, Tao Qin, Qing Tao, and Jue Wang. Robust sparse rank learning for non-smooth ranking measures. In SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pages 259-266, New York, NY, USA, 2009. ACM.
-
(2009)
SIGIR '09: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval
, pp. 259-266
-
-
Sun, Z.1
Qin, T.2
Tao, Q.3
Wang, J.4
|