Listwise approach to learning to rank: theory and algorithm
暂无分享,去创建一个
Tie-Yan Liu | Wensheng Zhang | Hang Li | Fen Xia | Jue Wang | Tie-Yan Liu | Fen Xia | Hang Li | Wensheng Zhang | Jue Wang
[1] Tong Zhang,et al. Statistical Analysis of Some Multi-Category Large Margin Classification Methods , 2004, J. Mach. Learn. Res..
[2] Ramesh Nallapati,et al. Discriminative models for information retrieval , 2004, SIGIR '04.
[3] Yoram Singer,et al. An Efficient Boosting Algorithm for Combining Preferences by , 2013 .
[4] Yi Lin,et al. Support Vector Machines and the Bayes Rule in Classification , 2002, Data Mining and Knowledge Discovery.
[5] Filip Radlinski,et al. A support vector method for optimizing average precision , 2007, SIGIR.
[6] J. Marden. Analyzing and Modeling Rank Data , 1996 .
[7] Hang Li,et al. AdaRank: a boosting algorithm for information retrieval , 2007, SIGIR.
[8] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[9] Klaus Obermayer,et al. Support vector learning for ordinal regression , 1999 .
[10] Gregory N. Hullender,et al. Learning to rank using gradient descent , 2005, ICML.
[11] D. Ruppert. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .
[12] Tie-Yan Liu,et al. Learning to rank: from pairwise approach to listwise approach , 2007, ICML '07.
[13] Tao Qin,et al. LETOR: Benchmark Dataset for Research on Learning to Rank for Information Retrieval , 2007 .
[14] Jaana Kekäläinen,et al. IR evaluation methods for retrieving highly relevant documents , 2000, SIGIR '00.
[15] Tao Qin,et al. Query-level loss functions for information retrieval , 2008, Inf. Process. Manag..
[16] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[17] Michael I. Jordan,et al. Convexity, Classification, and Risk Bounds , 2006 .
[18] Tong Zhang,et al. Subset Ranking Using Regression , 2006, COLT.