BoltzRank: Learning to maximize expected ranking gain

Proceedings of the 26th International Conference On Machine Learning, ICML 2009

Volumn , Issue , 2009, Pages 1089-1096

  Volkovs, Maksims N ^a   Zemel, Richard S ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BOLTZMANN DISTRIBUTION; CONDITIONAL PROBABILITY DISTRIBUTIONS; DATA SETS; ENERGY FUNCTIONS; EXPECTED VALUES; GRADIENT ASCENT; LEARNING APPROACH; LEARNING TO RANK; PAIRWISE CONSTRAINTS; PERFORMANCE MEASURE; RANKING FUNCTIONS; RANKING PERFORMANCE; RETRIEVED DOCUMENTS; SCORING FUNCTIONS; TEST TIME;

BOLTZMANN EQUATION; INFORMATION SERVICES; PROBABILITY; ROBOT LEARNING;

EDUCATION;

EID: 71149095619     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. R. Baeza-Yates and B. Ribeiro-Neto (1999). Modern Information Retrieval. Addison Wesley.
2. C. Burges (2006). Ranking As Function Approximation, In Algorithms for Approximation, Springer.
3. C. Burges, R. Rango and Q. V. Le (2006). Learning to rank with nonsmooth cost functions. Proceedings of the Neural Information Processing Systems, 395-402.
4. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender (2005). Learning to rank using gradient descent. Proceedings of the International Conference on Machine Learning, 89-96.
5. Z. Cao, T. Qin, T. Y. Liu, M. F. Tsai and H. Li (2007). Learning to rank: From pairwise approach to listwise approach. Proceedings of the International Conference on Machine Learning, 129-136.
6. K. Crammer and Y. Singer (2001). Pranking with ranking. Proceedings of the Neural Information Processing Systems, 641-647.
7. K. Jarvelin and J. Kekalainen (2000). IR evaluation methods for retrieving highly relevant documents. Proceedings of the Special Interest Group on Information Retrieval, 41-48.
8. Y. Freund, R. D. Iyer, R. E. Schapire and Y. Singer (2003). An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4:933-969.
9. T. Qin, T. Y. Liu, X. D. Zhang, D. S. Wang, H. Li (2008). Global Ranking Using Conditional Random Fields. Proceedings of the Neural Information Processing Systems, 1281-1288.
10. Y. T. Liu, T. Qin, J. Xu, Y. W. Xiong and H. Li (2007). Letor: Benchmark dataset for research on learning to rank for information retrieval. Proceedings of the Special Interest Group on Information Retrieval.
11. M. Taylor, J. Guiver, S. Robertson and T. Minka (2008). SoftRank: Optimizing non-smooth rank metrics. Proceedings of the Web Search and Data Mining, 77-86.
12. M. F. Tsai, T. Y. Liu, T. Qin, H. H. Chen, and W. Y. Ma (2007). FRank: A ranking method with delity loss. Proceedings of the Special Interest Group on Information Retrieval, 383-390.
13. J. Xu and H. Li (2007). AdaRank: A boosting algorithm for information retrieval. Proceedings of the Special Interest Group on Information Retrieval, 391-398.