Learning bilinear model for matching queries and documents

Journal of Machine Learning Research

Volumn 14, Issue , 2013, Pages 2519-2548

  Wu, Wei ^a   Lu, Zhengdong ^b   Li, Hang ^b  

^a MICROSOFT RESEARCH ASIA   (China)

^b HUAWEI NOAH S ARK LAB   (China)

Author keywords

Generalization analysis; Partial least squares; Regularized mapping to latent structures; Web search

Indexed keywords

GENERALIZATION ANALYSIS; HETEROGENEOUS DOMAINS; HETEROGENEOUS OBJECT; LATENT STRUCTURES; MAPPING FUNCTIONS; PARALLELIZATIONS; PARTIAL LEAST SQUARE (PLS); WEB SEARCHES;

KNOWLEDGE MANAGEMENT; LEAST SQUARES APPROXIMATIONS; MAPPING; WEBSITES;

INFORMATION RETRIEVAL;

EID: 84885600994     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (55)

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. F. Bach. Exploring large feature spaces with hierarchical multiple kernel learning. In NIPS'08, pages 105-112, 2008.
4. F. Bach, G. Lanckriet, and M. Jordan. Multiple kernel learning, conic duality, and the SMO algorithm. In ICML'04, 2004.
5. R. Baeza-Yates and A. Tiberi. Extracting semantic relations from query logs. In SIGKDD'07, pages 76-85, 2007. (Pubitemid 350229194)
6. B. Bai, J. Weston, D. Grangier, R. Collobert, K. Sadamasa, Y. Qi, O. Chapelle, and K. Weinberger. Supervised semantic indexing. In CIKM'09, pages 187-196, 2009.
7. P. L. Bartlett and S. Mendelson. Rademacher and gaussian complexities: Risk bounds and structural results. JMLR'02, 3:463-482, 2002.
8. C. Burges, R. Ragno, and Q. Le. Learning to rank with nonsmooth cost functions. In NIPS'06, pages 395-402. 2006.
9. 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.
10. 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.
11. T.Q. Chen,W.N. Zhang, Q.X. Lu, K.L. Chen, Z. Zhao, and Y. Yu. Svdfeature: A toolkit for featurebased collaborative filtering. JMLR'12, 13, 2012.
12. W. Chen, T.Y. Liu, and Z. Ma. Two-layer generalization analysis for ranking using rademacher average. NIPS'10, 23:370-378, 2010.
13. C. Cortes. Invited talk: Can learning kernels help performance? In ICML'09, page 161, 2009.
14. K. Crammer and Y. Singer. Pranking with ranking. In NIPS'01, pages 641-647, 2001.
15. N. Craswell and M. Szummer. Random walks on the click graph. In SIGIR'07, pages 239-246, 2007. (Pubitemid 350164967)
16. N. Cristianini, J. Shawe-taylor, A. Elisseeff, and J. Kandola. On kernel-target alignment. In NIPS'01, 2001.
17. J.V. Davis, B. Kulis, P. Jain, S. Sra, and I.S. Dhillon. Information-theoretic metric learning. In ICML'07, page 216, 2007.
18. S. Deerwester, S. Dumais, G. Furnas, T. Landauer, and R. Harshman. Indexing by latent semantic analysis. JASIS'90, 41:391-407, 1990.
19. J.H. Friedman. Greedy function approximation: a gradient boosting machine. Ann. Statist, 29(5): 1189-1232, 2001. (Pubitemid 33405972)
20. J. Gao, K. Toutanova, and W. Yih. Clickthrough-based latent semantic models for web search. In SIGIR'11, pages 675-684, 2011.
21. D. Grangier and S. Bengio. A discriminative kernel-based model to rank images from text queries. IEEE Transactions on PAMI, 30(8):1371-1384, 2008.
22. D.R. Hardoon, S. Szedmak, and J. Shawe-Taylor. Canonical correlation analysis: An overview with application to learning methods. Neural Computation, 16(12):2639-2664, 2004. (Pubitemid 39604012)
23. R. Herbrich, T. Graepel, and K. Obermayer. Large margin rank boundaries for ordinal regression. NIPS'99, pages 115-132, 1999.
24. T. Hertz, A. Bar-Hillel, and D. Weinshall. Boosting margin based distance functions for clustering. In ICML'04, page 50, 2004.
25. T. Hofmann. Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst., 22:89-115, 2004.
26. S. CH. Hoi, W. Liu, M.R. Lyu, and W.Y. Ma. Learning distance metrics with contextual constraints for image retrieval. In CVPR'06, volume 2, pages 2072-2078, 2006. (Pubitemid 44931570)
27. S. JacobGoldberger and R. GeoffHinton. Neighbourhood components analysis. NIPS'04, 2004.
28. K. Jarvelin and J. Kekalainen. Ir evaluation methods for retrieving highly relevant documents. In SIGIR'00, pages 41-48, 2000.
29. T. Joachims. Optimizing search engines using clickthrough data. In KDD '02, pages 133-142, 2002.
30. I.T. Jolliffe. Principal Component Analysis. Springer verlag, 2002.
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. H. Li. Learning to rank for information retrieval and natural language processing. Synthesis Lectures on Human Language Technologies, 4(1):1-113, 2011.
33. T.Y. Liu. Learning to rank for information retrieval. Foundations and Trends in Information Re- trieval, 3(3):225-331, 2009.
34. H. Ma, H.X. Yang, I. King, and M. R. Lyu. Learning latent semantic relations from clickthrough data for query suggestion. In CIKM'08, pages 709-718, 2008.
35. C. Micchelli and M. Pontil. Learning the kernel function via regularization. JMLR'05, 6:1099-1125, 2005.
36. C.S. Ong, A.J. Smola, and R.C. Williamson. Learning the kernel with hyperkernels. JMLR '05, 6: 1043-1071, 2005.
37. J.M. Ponte and W.B. Croft. A language modeling approach to information retrieval. In SIGIR'98, pages 275-281, 1998.
38. S. E. Robertson, S. Walker, S. Jones, M. Hancock-Beaulieu, and M. Gatford. Okapi at trec-3. In TREC, 1994.
39. R. Rosipal and N. Krämer. Overview and recent advances in partial least squares. Subspace, Latent Structure and Feature Selection, pages 34-51, 2006. (Pubitemid 44029878)
40. 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.
41. G. Salton and M. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York, NY, USA, 1986.
42. P.J. Schreier. A unifying discussion of correlation analysis for complex random vectors. Signal Processing, IEEE Transactions on, 56(4):1327-1336, 2008. (Pubitemid 351493830)
43. M. Schultz and T. Joachims. Learning a distance metric from relative comparisons. NIPS'03, 2003.
44. N. Srebro, J.D.M. Rennie, and T. Jaakkola. Maximum-margin matrix factorization. NIPS'05, pages 1329-1336, 2005.
45. M. Sugiyama. Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. JMLR'07, 8:1027-1061, 2007. (Pubitemid 46798409)
46. M. Varma and B. R. Babu. More generality in efficient multiple kernel learning. In ICML'09, page 134, 2009.
47. J.A. Wegelin. A survey of partial least squares (pls) methods, with emphasis on the two-block case. Technical Report, No.371, Seattle: Department of Statistics, Univ. of Wash., 2000.
48. K.Q. Weinberger and L.K. Saul. Distance metric learning for large margin nearest neighbor classification. JMLR'09, 10:207-244, 2009.
49. W. Wu, J. Xu, H. Li, and O. Satoshi. Learning a robust relevance model for search using kernel methods. JMLR'11, 12:1429-1458, 2011.
50. E.P. Xing, A.Y. Ng, M.I. Jordan, and S. Russell. Distance metric learning with application to clustering with side-information. NIPS'03, pages 521-528, 2003.
51. J. Xu and H. Li. Adarank: a boosting algorithm for information retrieval. In SIGIR' 07, pages 391-398, 2007. (Pubitemid 350164985)
52. J. Xu, H. Li, and Z.L. Zhong. Relevance ranking using kernels. In AIRS '10, 2010.
53. L. Yang, R. Jin, R. Sukthankar, and Y. Liu. An efficient algorithm for local distance metric learning. In AAAI'06, page 543, 2006.
54. Y. Ying, K. Huang, and C. Campbell. Sparse Metric Learning via Smooth Optimization. NIPS'09, pages 521-528, 2009.
55. 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.