Learning a robust relevance model for search using kernel methods

Journal of Machine Learning Research

Volumn 12, Issue , 2011, Pages 1429-1458

  Wu, Wei ^a   Xu, Jun ^b   Li, Hang ^b   Oyama, Satoshi ^c  

^a PEKING UNIVERSITY   (China)

^b MICROSOFT RESEARCH ASIA   (China)

^c HOKKAIDO UNIVERSITY   (Japan)

Author keywords

Kernel machines; S function; S kernel; Search; Similarity learning; Term mismatch

Indexed keywords

KERNEL MACHINE; S-FUNCTION; S-KERNEL; SEARCH; SIMILARITY LEARNING; TERM MISMATCH;

HILBERT SPACES; INFORMATION RETRIEVAL; USER INTERFACES;

WORLD WIDE WEB;

EID: 79960145806     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (52)

1. J. Abernethy, F. Bach, T. Evgeniou, and J.P. Vert. A new approach to collaborative filtering: Operator estimation with spectral regularization. JMLR '09, 10:803-826, 2009.
2. S. Agarwal and P. Niyogi. Stability and generalization of bipartite ranking algorithms. In COLT'05, pages 32-47, 2005.
3. E. Agichtein, E. Brill, and S. Dumais. Improving web search ranking by incorporating user behavior information. In SIGIR '06, pages 19-26, 2006.
4. F. Bach. Exploring large feature spaces with hierarchical multiple kernel learning. In NIPS'08, pages 105-112, 2008.
5. F. Bach, G. Lanckriet, and M. Jordan. Multiple kernel learning, conic duality, and the SMO algorithm. In ICML'04, 2004.
6. R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1999.
7. J. Basilico and T. Hofmann. Unifying collaborative and content-based filtering. In ICML '04, pages 65-72, 2004. (Pubitemid 40290793)
8. D. Beeferman and A. Berger. Agglomerative clustering of a search engine query log. In KDD'00, pages 407-416, 2000.
9. A. Broder, P. Ciccolo, E. Gabrilovich, V. Josifovski, D. Metzler, L. Riedel, and J. Yuan. Online expansion of rare queries for sponsored search. In WWW'09, pages 511-520, 2009.
10. C. Burges, R. Ragno, and Q. Le. Learning to rank with nonsmooth cost functions. In NIPS'06, pages 395-402. 2006. 1455
11. Y. Cao, J. Xu, T.Y. Liu, H. Li, Y. Huang, and H.W. Hon. Adapting ranking SVM to document retrieval. In SIGIR '06, pages 186-193, 2006.
12. Z. Cao, T. Qin, T.Y. Liu, M.F. Tsai, and H. Li. Learning to rank: From pairwise approach to listwise approach. In ICML '07, pages 129-136, 2007.
13. C. Cortes. Invited talk: Can learning kernels help performance? In ICML'09, page 161, 2009.
14. T. Cover and P. Hart. Nearest neighbor pattern classification. IEEE Trans. on Information Theory, 13(1):21-27, 1967.
15. K. Crammer and Y. Singer. Pranking with ranking. In NIPS'01, pages 641-647, 2001.
16. N. Craswell and M. Szummer. Random walks on the click graph. In SIGIR' 07, page 246, 2007.
17. H. Cui, J. Wen, J. Nie, and W. Ma. Query expansion by mining user logs. IEEE Trans. on Knowl. and Data Eng., 15(4):829-839, 2003.
18. S. Deerwester, S. Dumais, G. Furnas, T. Landauer, and R. Harshman. Indexing by latent semantic analysis. JASIS, 41:391-407, 1990.
19. S. Dudani. The distance-weighted k-nearest-neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics, 6(4):325-327, April 1976.
20. Y. Freund, R. D. Iyer, R. E. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. JMLR'03, 4:933-969, 2003.
21. T. Gartner, P. Flach, and S. Wrobel. On graph kernels: Hardness results and efficient alternatives. In COLT '03, page 129, 2003.
22. D. Grangier and S. Bengio. A discriminative kernel-based model to rank images from text queries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(8):1371-1384, 2008.
23. D. Haussler. Convolution kernels on discrete structures. Technical Report UCSC-CRL-99-10, Computer Science Dept., UC Santa Cruz., 1999.
24. R. Herbrich, T. Graepel, and K. Obermayer. Large margin rank boundaries for ordinal regression. NIPS'99, pages 115-132, 1999.
25. T. Hofmann. Probabilistic latent semantic indexing. In SIGIR' 99, pages 50-57, 1999.
26. T. Hofmann, B. Scholkopf, and A.J. Smola. Kernel methods in machine learning. Annals of Statistics, 36(3):1171, 2008.
27. K. Jarvelin and J. Kekalainen. Ir evaluation methods for retrieving highly relevant documents. In SIGIR' 00, pages 41-48, 2000.
28. T. Joachims. Optimizing search engines using clickthrough data. In KDD '02, pages 133-142, 2002.
29. H. Kashima, K. Tsuda, and A. Inokuchi. Kernels for graphs. Kernel Methods in Computational Biology, pages 155-170, 2004. 1456
30. N. Koide and Y. Yamashita. Asymmetric kernel method and its application to Fisher's discriminant. In ICPR '06, pages 820-824, 2006.
31. G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. E. Ghaoui, and M. I. Jordan. Learning the kernel matrix with semi-definite programming. In ICML'02, pages 323-330, 2002.
32. T.Y. Liu. Learning to rank for information retrieval. Found. Trends Inf. Retr., 3(3):225-331, 2009.ISSN 1554-0669.
33. C. Micchelli and M. Pontil. Learning the kernel function via regularization. JMLR'05, 6:1099-1125, 2005.
34. M. Mitra, A. Singhal, and C. Buckley. Improving automatic query expansion. In SIGIR '98, pages 206-214, 1998.
35. C.S. Ong, A.J. Smola, and R.C. Williamson. Learning the kernel with hyperkernels. JMLR '05, 6: 1043-1071, 2005.
36. J.M. Ponte and W.B. Croft. A language modeling approach to information retrieval. In SIGIR' 98, pages 275-281, 1998.
37. S. E. Robertson, S. Walker, S. Jones, M. Hancock-Beaulieu, and M. Gatford. Okapi at trec-3. In TREC, 1994.
38. C. Rudin, C. Cortes, M. Mohri, and R. E. Schapire. Margin-based ranking meets boosting in the middle. In COLT'05, pages 63-78, 2005.
39. G. Salton and C. Buckley. Improving retrieval performance by relevance feedback. Readings in Information Retrieval, pages 355-364, 1997.
40. G. Salton and M. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York, NY, USA, 1986. ISBN 0070544840.
41. B. Schölkopf and A.J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. the MIT Press, 2002.
42. V.N. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag New York, Inc., New York, NY, USA, 1995. ISBN 0-387-94559-8.
43. M. Varma and B. R. Babu. More generality in efficient multiple kernel learning. In ICML'09, page 134, 2009.
44. S.V.N. Vishwanathan and A.J. Smola. Binet-cauchy kernels. In NIPS '04, 2004.
45. C. Watkins. Dynamic alignment kernels. In NIPS '99, 1999.
46. J.R. Wen, J.Y. Nie, and H.J. Zhang. Query clustering using user logs. ACM Trans. Inf. Syst., 20(1): 59-81, 2002. ISSN 1046-8188.
47. J. Xu and W.B. Croft. Query expansion using local and global document analysis. In SIGIR '96, pages 4-11, 1996.
48. J. Xu and H. Li. Adarank: a boosting algorithm for information retrieval. In SIGIR' 07, pages 391-398, 2007. (Pubitemid 350164985)
49. J. Xu, H. Li, and Z.L. Zhong. Relevance ranking using kernels. In AIRS '10, 2010.
50. C.X. Zhai and J. Lafferty. A study of smoothing methods for language models applied to information retrieval. ACM Trans. Inf. Syst., 22(2):179-214, 2004.
51. S.K. Zhou. Trace and determinant kernels between matrices. In NIPS '04, 2004.
52. Z.M. Zhuang and S. Cucerzan. Re-ranking search results using query logs. In CIKM '06, pages 860-861, 2006. (Pubitemid 47203665)