Heterogeneous cross domain ranking in latent space

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2009, Pages 987-996

  Wang, Bo ^a   Tang, Jie ^b   Fan, Wei ^c   Chen, Songcan ^a   Yang, Zi ^b   Liu, Yanzhu ^d  

^a NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

^b TSINGHUA UNIVERSITY   (China)

^c IBM T J WATSON RESEARCH CENTER   (United States)

^d PEKING UNIVERSITY   (China)

Author keywords

Heterogeneous cross domain ranking; Learning to rank; Transfer ranking

Indexed keywords

CONVEX UPPER BOUND; CROSS-DOMAIN; DATA SETS; DATA SOURCE; GENERALIZATION BOUND; HETEROGENEOUS DOMAINS; INFORMATION SOURCES; LEARNING TO RANK; LOSS FUNCTIONS; RAPID GROWTH; REAL-WORLD APPLICATION; TRAINING DATA; WEB 2.0;

KNOWLEDGE MANAGEMENT;

EID: 74549186512     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1645953.1646079     Document Type: Conference Paper

References (31)

1. M.-R. Amini, T.-V. Truong, and C. Goutte. A boosting algorithm for learning bipartite ranking functions with partially labeled data. In SIGIR'08, pages 99-106, July 2008.
2. A. Argyriou, T. Evgeniou, and M. Pontil. Multi-task feature learning. In NIPS'06, pages 41-48, 2006.
3. R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. ACM Press, 1999.
4. S. Bickel, M. Brüuckner, and T. Scheffer. Discriminative learning for differing training and test distributions. In ICML'07, pages 81-88, 2007.
5. J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Wortman. Learning bounds for domain adaptation. In NIPS'07, pages 129-136, 2007.
6. J. Blitzer, R. McDonald, and F. Pereira. Domain adaptation with structural correspondence learning. In EMNLP'06, pages 120-128, 2006.
7. E. Bonilla, K. M. Chai, and ChrisWilliams. Multi-task gaussian process prediction. In NIPS'08, pages 153-160, 2008.
8. U. Brefeld and T. Scheffer. Auc maximizing support vector learning. In Proceedings of ICML'05 workshop on ROC Analysis in Machine Learning, 2005.
9. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In ICML'05, pages 89-96, 2005.
10. K. Chen, R. Lu, C. K. Wong, G. Sun, L. Heck, and B. Tseng. Trada: tree based ranking function adaptation. In CIKM'08, pages 1143-1152, 2008.
11. C.-T. Chu, S. K. Kim, Y.-A. Lin, Y. Yu, G. Bradski, A. Y. Ng, and K. Olukotun. Map-reduce for machine learning on multicore. In NIPS'06, pages 281-288, 2006.
12. W. Dai, Q. Yang, G.-R. Xue, and Y. Yu. Boosting for transfer learning. In ICML'07, pages 193-200, 2007.
13. K. Duh and K. Kirchhoff. Learning to rank with partially-labeled data. In SIGIR'08, pages 251-258, July 2008.
14. J. Gao, W. Fan, J. Jian, and J. Han. Knowledge transfer via multiple model local structure mapping. In KDD'08, pages 283-291, 2008.
15. R. Herbrich, T. Graepel, and K. Obermayer. Large margin rank boundaries for ordinal regression. MIT Press, Cambridge, MA, 2000.
16. S. C. Hoi and R. Jin. Semi-supervised ensemble ranking. In AAAI'08, July 2008.
17. K. Jarvelin and J. Kekalainen. Ir evaluation methods for retrieving highly relevant documents. In SIGIR'00, pages 41-48, 2000.
18. T. Jebara. Multi-task feature and kernel selection for svms. In ICML'04, July 2004.
19. T. Joachims. Learning to classify text using support vector machines. Dissertation, 2002.
20. T. Joachims. Training linear svms in linear time. In KDD'06, pages 217-226, 2006.
21. S.-I. Lee, V. Chatalbashev, D. Vickrey, and D. Koller. Learning a meta-level prior for feature relevance from multiple related tasks. In ICML'07, pages 489-496, July 2007.
22. J. Liu, S. Ji, and J. Ye. Accelerated multi-task feature learning. In UAI'09, June 2009.
23. T.-Y. Liu, J. Xu, T. Qin, W. Xiong, and H. Li. Letor: Benchmark dataset for research on learning to rank for information retrieval. In LR4IR 2007, in conjunction with SIGIR 2007, 2007.
24. R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng. Self-taught learning: Transfer learning from unlabeled data. In ICML'07, pages 759-766, June 2007.
25. J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang, and Z. Su. Arnetminer: Extraction and mining of academic social networks. In KDD'08, pages 990-998, 2008.
26. M. E. Wall, A. Rechtsteiner, and L. M. Rocha. Singular value decomposition and principal component analysis, pages 91-109. Kluwer: Norwell, MA, 2003.
27. J. Xu and H. Li. Adarank: a boosting algorithm for information retrieval. In SIGIR'07, pages 391-398, 2007.
28. Z. Yang, J. Tang, B. Wang, J. Guo, J. Li, and S. Chen. Expert2bole: From expert finding to bole search. In KDD'09, 2009.
29. Y. Yue, T. Finley, F. Radlinski, and T. Joachims. A support vector method for optimizing average precision. In SIGIR'07, pages 271-278, 2007.
30. C. Zhai and J. Lafferty. Model-based feedback in the language modeling approach to information retrieval. In CIKM'01, pages 403-410, 2001.
31. J. Zhang, J. Tang, and J. Li. Expert finding in a social network. In DASFAA'07, pages 1066-1069, 2007.