Learning to select a ranking function

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5993 LNCS, Issue , 2010, Pages 114-126

  Peng, Jie ^a   MacDonald, Craig ^a   Ounis, Iadh ^a  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION RETRIEVAL; SEARCH ENGINES;

ADARANK; AUTOMATIC FEATURE SELECTION; DOCUMENT RANKING; FEATURE SETS; RANKING FUNCTIONS; RANKING SVM; RETRIEVAL PERFORMANCE; STATE OF THE ART;

LEARNING TO RANK;

EID: 77952316994     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-12275-0_13     Document Type: Conference Paper

References (20)

1. Xu, J., Li, H.: AdaRank: A Boosting Algorithm for Information Retrieval. In: Proceedings of SIGIR 2007, Amsterdam, The Netherlands (2007)
2. Metzler, D.: Automatic Feature Selection in the Markov Random Field Model for Information Retrieval. In: Proceedings of CIKM 2007, Lisbon, Portugal (2007)
3. Geng, X., Liu, T.Y., Qin, T., Arnold, A., Li, H., Shum, H.Y.: Query Dependent Ranking Using K-Nearest Neighbour. In: Proceedings of SIGIR 2008, Singapore (2008)
4. Liu, T.Y., Qin, T., Xu, J., Xiong, W.Y., Li, H.: LETOR: Benchmark Dataset for Research on Learning to Rank for Information Retrieval. In: Proceedings of SIGIR 2007 Learning to Rank workshop, Amsterdam, The Netherlands (2007)
5. Herbrich, R., Graepel, T., Obermayer, K.: Large Margin Rank Boundaries for Ordinal Regression. MIT Press, Cambridge (2000)
6. Joachims, T.: Optimizing Search Engines using Clickthrough Data. In: Proceedings of SIGKDD 2002, Alberta, Canada (2002)
7. Kamps, J., Mishne, G., de Rijke, M.: Language Models for Searching in Web Corpora. In: Proceedings of TREC 13, Gaithersburg, MD, USA (2004)
8. Peng, J., He, B., Ounis, I.: Predicting the Usefulness of Collection Enrichment for Enterprise Search. In: Azzopardi, L., Kazai, G., Robertson, S., R̈uger, S., Shokouhi, M., Song, D., Yilmaz, E. (eds.) ICTIR 2009. LNCS, vol.5766, pp. 366-370. Springer, Heidelberg (2009)
9. Plachouras, V., Ounis, I.: Usefulness of Hyperlink Structure for Query-Biased Topic Distillation. In: Proceedings of SIGIR 2004, Sheffield, UK (2004)
10. Plachouras, V.: Selective Web Information Retrieval. PhD thesis, University of Glasgow, UK (2006)
11. Peng, J., Ounis, I.: Selective Application of Query-Independent Features in Web Information Retrieval. In: Proceedings of ECIR 2009, Toulouse, France (2009)
12. Manmatha, R., Rath, T., Feng, F.: Modeling Score Distributions for Combining the Outputs of Search Engines. In: Proceedings of SIGIR 2001, New Orleans LA, USA (2001)
13. Xue, G.R., Yang, Q., Zeng, H.J., Yu, Y., Chen, Z.: Exploiting the Hierarchical Structure for Link Analysis. In: Proceedings of SIGIR 2005, Salvador, Brazil (2005)
14. Song, R., Wen, J.R., Shi, S., Xin, G., Liu, T.Y., Qin, T., Zheng, X., Zhang, J., Xue, G., Ma, W.Y.: Microsoft Research Asia at Web Track and Terabyte Track of TREC 2004. In: Proceedings of TREC 2004, Gaithersburg, MD, USA (2004)
15. Yang, K., Yu, N., Wead, A., La Rowe, G., Li, Y.H., Friend, C., Lee, Y.: WIDIT in TREC 2004 Genomics, Hard, Robust and Web Tracks. In: Proceedings of TREC 2004, Gaithersburg, MD, USA (2004)
16. Craswell, N., Hawking, D.: Overview of the TREC 2004Web Track. In: Proceedings of TREC 2004, Gaithersburg, MD, USA (2004)
17. Kullback, S.: Information Theory and Statistics. John Wiley & Sons, New York (1959)
18. Lin, J.: Divergence Measures Based on the Shannon Entropy. IEEE Transactions on Information Theory 37(1), 145-151 (1991)
19. Lee, J.H.: Analyses of Multiple Evidence Combination. In: Proceedings of SIGIR 1997, Philadelphia, USA (1997)
20. Kirkpatrick, S., Gelatt, C., Vecchi, M.: Optimization by Simulated Annealing. Science 220(4598), 671-680 (1983)