Learning to rank with softrank and gaussian processes

ACM SIGIR 2008 - 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Proceedings

Volumn , Issue , 2008, Pages 259-266

  Guiver, John ^a   Snelson, Edward ^a  

^a MICROSOFT RESEARCH   (United States)

Author keywords

Gaussian process; Information retrieval; Learning; Ranking

Indexed keywords

GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); IMAGE SEGMENTATION; INFORMATION RETRIEVAL; INFORMATION SERVICES; LINEAR REGRESSION; NEURAL NETWORKS; PROBABILITY; REGRESSION ANALYSIS; RESEARCH AND DEVELOPMENT MANAGEMENT; TRELLIS CODES;

BASELINE METHODS; DATA SETS; DISCOUNTED CUMULATIVE GAINS; DOCUMENT RANKINGS; DOCUMENT RETRIEVALS; GAUSSIAN PROCESS; GAUSSIAN PROCESSES; IMPROVED PERFORMANCES; LEARNING; LEARNING TO RANKS; MODEL PERFORMANCES; NON-LINEAR REGRESSIONS; OHSUMED; OPTIMISATION; RANDOM NOISES; RANKING; TRAINING SETS;

PROBABILITY DISTRIBUTIONS;

EID: 57349126318     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390334.1390380     Document Type: Conference Paper

References (18)

1. C. Burges, R. Ragno, and Q. V. L. Le. Learning to rank with nonsmooth cost functions. In NIPS, 2006.
2. C Burges, T. Snaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hüllender. Learning to rank using gradient descent. In ICML, 2005.
3. W. Chu and Z. Ghahramani. Gaussian processes for ordinal regression. JMLR, 6:1019-1041, 2005.
4. K. Crammer and Y. Singer. Pranking with ranking. In NIPS 14, 2002.
5. R. Herbrich, T. Graepel, and K. Obermayer. Large margin rank boundaries for ordinal regression. In Advances in Large Margin Classifiers, pages 115-132. MIT Press, 2000.
6. K. Järvelin and J. Kekäläinen. IR evaluation methods for retrieving highly relevant documents. In SIGIR, 2000.
7. T. Joachims. Optimizing search engines using clickthrough data. In KDD, 2002.
8. N. D. Lawrence. Learning for larger datasets with the Gaussian process latent variable model. In M. Meila and X. Shen, editors, AISTATS 11. Omnipress, 2007.
9. T.-Y. Liu. LETOR: Benchmark datasets for learning to rank, 2007. Microsoft Research Asia. http://research.microsoft.com/users/LETOR/.
10. R. M. Neal. Bayesian learning for neural networks. In Lecture Notes in Statistics 118. Springer, 1996.
11. J. Nocedal and S. Wright. Numerical Optimization, Second Edition. Springer, 2006.
12. J. Quiñonero Candela and C. E. Rasmussen. A unifying view of sparse approximate Gaussian process regression. JMLR, 6:1939-1959, Dec 2005.
13. C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. MIT press, 2006.
14. S. Robertson and H. Zaragoza. On rank-based effectiveness measures and optimization. Information Retrieval, 10(3):321-339, 2007.
15. S. Robertson, H. Zaragoza, and M. Taylor. A simple BM 25 extension to multiple weighted fields. In CIKM, pages 42-29, 2004.
16. E. Srielson and Z. Ghahramani. Sparse Gaussian processes using pseudo-inputs. In Y. Weiss, B. Schölkopf, and J. Piatt, editors, NIPS 18, pages 1257-1264. MIT press, Cambridge, MA, 2006.
17. M. Taylor, J. Guiver, S. Robertson, and T. Minka. SoftRank: optimizing non-smooth rank metrics. In WSDM '08, pages 77-86. ACM, 2008.
18. L. L. Thurstone. A law of comparative judgement. Psychological Reviews, 34:273-286, 1927.