A survey on learning to rank

Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC

Volumn 3, Issue , 2008, Pages 1734-1739

  He, Chuan ^a   Wanq, Cong ^a   Zhonq, Yi Xin ^a   Li, Rui Fan ^a  

^a BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

Evaluation; Information retrieval support vector machine; Learning to rank; Ordinal regression; Ranking

Indexed keywords

CONTROL THEORY; CYBERNETICS; EDUCATION; IMAGE RETRIEVAL; INFORMATION RETRIEVAL; INFORMATION SERVICES; LEARNING SYSTEMS; REGRESSION ANALYSIS; ROBOT LEARNING; SUPPORT VECTOR MACHINES; VECTORS;

APPLICATIONS.; EFFECTIVE ALGORITHMS; EVALUATION; FRAMEWORK AND ALGORITHMS; KEY PROBLEMS; LEARNING PROBLEMS; LEARNING TO RANK; ON MACHINES; ORDINAL REGRESSION; RANKING; RANKING METHODS;

LEARNING ALGORITHMS;

EID: 57749088854     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLC.2008.4620685     Document Type: Conference Paper

References (24)

1. R. Baeza-Yates, and B. Ribeiro-Neto, Modern information retrieval. Addison Wiley, 1999.
2. Ralf Herbrich, Thore Graepel, and Klaus Obermayer. Large margin rank boundaries for ordinal regression. Advances in Large Margin Classifiers, MIT Press. 2000.
3. Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. Learning to rank using gradient descent. Proceedings of International Conference on Machine Learning, 2005.
4. Ming-Feng Tsai, Tie-Yan Liu, Tao Qin, Hsin-Hsi Chen, and Wei-Ying Ma. FRank: a ranking method with fidelity loss. Proceedings of ACM SIGIR conference on Reseach and development in information retrieval, 2007.
5. Rich Caruana, Shumeet Baluja, and Tom Mitchell. Using the feature to "sort out" the present: Rankprop and Multitask learning for medical risk evaluation. Advances on Neural Information Processing System, 1996.
6. Ofer Dekel, Christopher Manning, and Yoram Singer. Log-Linear models for label ranking. Advances on Neural Information Processing System, 2003.
7. Koby Crammer and Yoram Singer. Pranking with ranking. Advances on Neural Information Processing System, 2002.
8. Edward Harrington, Online ranking/collaborative filtering using the perceptron algorithm. Proceedings of the 20th International Conference on Machine Learning, 2003.
9. Christopher Burges, Robert Ragno, and Quoc Viet Le, Learning to rank with nonsmooth cost functions. Advances on Neural Information Processing System, 2006.
10. Wei Chu, and Sathiya Keerthi, New approaches to support vector ordinal regression. Proceedings of the 22nd International Conference on Machine Learning, 2005.
11. Thorsten Joachims, Optimizing searching engines using click-through data. Proceedings of the ACM conference on Knowledge Discovery and Data Mining, 2002.
12. Yunbo Cao, Jun Xu, Tie-Yan Liu, Hang Li, Yalou Huang, and Hsiao-Wuen Hon, Adapting ranking SVM to document retrieval. Proceedings of the ACM SIGIR conference, 2006.
13. Jun Xu, and Hang Li, AdaRank: a boosting algorithm for information retrieval. Proceedings of the ACM SIGIR conference, 2007.
14. Tie-Yan Liu, Jun Xu, Tao Qin, Wernying Xiong, and Hang Li, LETOR: Benchmark dataset for research on learning to rank for information retrieval. Proceedings of the ACM SIGIR conference, 2007.
15. Irina Matveeva, Chris Burges, and Timo Burkard, High accuracy retrieval with multiple nested ranker. Proceedings of the ACM SIGIR conference, 2006.
16. Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li, Learning to rank: from pairwise approach to listwise approach. Proceedings of International Conference on Machine Learning, 2007.
17. Xiubo Geng, Tie-Yan Liu, Tao Qin, and Hang Li, Feature selection for ranking. Proceedings of the ACM SIGIR conference, 2007.
18. Michael Taylor, Hugo Zaragoza, and Nick Vraswell, Optimisation methods for ranking functions with multiple parameters, Proceedings of the ACM CIKM(the ACM Conference on Information and Knowledge Management), 2006.
19. Filip Radinski, and Thorsten Joachims, Query Chains: learning to rank from implicit feedback. Proceedings of the ACM SIGKDD, 2005.
20. K. Jarvelin, and J. Kekalainen. IR evaluation methods for retrieving highly relevant documents. Proceedings of the ACM SIGIR conference, 2000.
21. K. Jarvelin, and J. Kekalainen, Cumulated gain-based evaluation of IR techniques, ACM Transactions on Information Systems, 2002.
22. R. D. Luce, Individual choice behavior. Wiley, 1959.
23. R. L. Rlackett, The analysis of permutations. Applied Statistics, 1975.
24. Ralf Herbrich, Thore Graepel, Peter Bollmann-Sdorra, and Klaus Obermayer. Learning preference relations for information retrieval. AAAI, 1998.