On the local optimality of LambdaRank

Proceedings - 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009

Volumn , Issue , 2009, Pages 460-467

  Donmez, Pinar ^a   Svore, Krysta M ^b   Burges, Christopher J C ^b  

^a Carnegie Mellon University   (United States)

^b MICROSOFT RESEARCH   (United States)

Author keywords

Learning to rank; Web search

Indexed keywords

EVALUATION MEASURES; IR TARGETS; LEARNING TO RANK; LOCAL OPTIMALITY; MACHINE-LEARNING; OPTIMAL SOLUTIONS; OPTIMIZATION MEASURES; TRAINING DATA; TRAINING SETS; WEB SEARCHES;

EDUCATION; INFORMATION RETRIEVAL; INFORMATION SERVICES; LOCOMOTIVES; OPTIMIZATION; RAILROAD CARS; TARGETS;

WORLD WIDE WEB;

EID: 72449208539     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1571941.1572021     Document Type: Conference Paper

References (13)

1. C.J.C. Burges, R. Ragno, and Q.V. Le. Learning to rank with nonsmooth cost functions. In Neural Information Processing Systems (NIPS), 2006. See also MSR Technical Report MSR-TR-2006-60.
2. C.J.C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In International Conference on Machine Learning (ICML), Bonn, Germany, 2005.
3. Z. Cao, T. Qin, T.Y. Liu, M.F. Tsai, and H. Li. Learning to rank: From pairwise to listwise approach. In International Conference on Machine Learning (ICML), pages 129-136, 2007.
4. K. Crammer and Y. Singer. Pranking with ranking. In Neural Information Processing Systems (NIPS), 2001.
5. R. Herbrich, T. Graepel, and K. Obermayer. Large margin rank boundaries for ordinal regression. Advances in Large Margin Classifiers, pages 115-132, 2000.
6. T. Qin, T.Y. Liu, and H. Li. A general approximation framework for direct optimization of information retrieval measures. Microsoft Technical Report MSR-TR-2008-164, 2008.
7. T. Qin, X.-D. Zhang, M.-F. Tsai, D.-S. Wang, T.-Y. Liu, and H. Li. Query-level loss functions for information retrieval. Information Processing and Management, 44(2):838-855, 2007.
8. S. Robertson and H. Zaragoza. On rank-based effectiveness measures and optimization. Information Processing and Management, 10:321-339, 2007.
9. B. Taskar, V. Chatalbashev, D. Koller, and C. Guestrin. Learning structured prediction models: A large margin approach. In International Conference on Machine Learning (ICML), Bonn, Germany, 2005.
10. I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun. Support vector machine learning for interdependent and structured output spaces. In International Conference on Machine Learning (ICML), 2004.
11. J. Xu and H. Li. Adarank: A boosting algorithm for information retrieval. In ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pages 391-398, 2007.
12. Y. Yue and C.J.C Burges. On using simultaneous perturbation stochastic approximation for ir measures, and the empirical optimality of lambdarank. NIPS Machine Learning for Web Search Workshop, 2007.
13. Y. Yue, T. Finley, F. Radlinski, and T. Joachims. A support vector method for optimizing average precision. In ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2007.