Fast pairwise query selection for large-scale active learning to rank

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2013, Pages 607-616

  Qian, Buyue ^a   Wang, Xiang ^a   Wang, Jun ^a   Li, Hongfei ^a   Cao, Nan ^a   Zhi, Weifeng ^b   Davidson, Ian ^b  

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

^b University of California Davis   (United States)

Author keywords

Active Learning; Hashing; Learning to Rank

Indexed keywords

EID: 84894662428     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2013.54     Document Type: Conference Paper

References (30)

1. L. Hang, "A short introduction to learning to rank," IEICE TRANSACTIONS on Information and Systems, vol. 94, no. 10, pp. 1854-1862, 2011.
2. A. Shashua and A. Levin, "Ranking with large margin principle: Two approaches," in NIPS, 2002, pp. 937-944.
3. K. Crammer and Y. Singer, "Pranking with ranking," in NIPS, 2001, pp. 641-647.
4. D. Cossock and T. Zhang, "Subset ranking using regression," in COLT, 2006, pp. 605-619.
5. Z. Zheng, K. Chen, G. Sun, and H. Zha, "A regression framework for learning ranking functions using relative relevance judgments," in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, ser. SIGIR '07. New York, NY, USA: ACM, 2007, pp. 287-294.
6. C. J. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. N. Hullender, "Learning to rank using gradient descent," in ICML, 2005, pp. 89-96.
7. D. Parikh and K. Grauman, "Relative attributes," in ICCV, 2011, pp. 503-510.
8. Z. Cao, T. Qin, T.-Y. Liu, M.-F. Tsai, and H. Li, "Learning to rank: from pairwise approach to listwise approach," in Proceedings of the 24th international conference on Machine learning, ser. ICML '07. New York, NY, USA: ACM, 2007, pp. 129-136.
9. J. Xu and H. Li, "Adarank: a boosting algorithm for information retrieval," in SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. New York, NY, USA: ACM, 2007, pp. 391-398.
10. Y. Yue, T. Finley, F. Radlinski, and T. Joachims, "A support vector method for optimizing average precision," in ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2007, pp. 271-278.
11. T. Joachims, "Optimizing search engines using clickthrough data," in Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, ser. KDD '02. New York, NY, USA: ACM, 2002, pp. 133-142. [Online]. Available: http://doi.acm.org/10.1145/775047.775067
12. B. Long, O. Chapelle, Y. Zhang, Y. Chang, Z. Zheng, and B. Tseng, "Active learning for ranking through expected loss optimization," in Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, ser. SIGIR '10. New York, NY, USA: ACM, 2010, pp. 267-274. [Online]. Available: http://doi.acm.org/10.1145/1835449. 1835495
13. B. Settles, "Active learning literature survey," University of Wisconsin-Madison, Computer Sciences Technical Report 1648, 2009.
14. H. Yu, "Svm selective sampling for ranking with application to data retrieval," in Proceedings of the 11th ACM SIGKDD international conference on Knowledge discovery in data mining, 2005, pp. 354-363.
15. J. A. Aslam, E. Kanoulas, V. Pavlu, S. Savev, and E. Yilmaz, "Document selection methodologies for efficient and effective learning-to-rank," in Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, ser. SIGIR '09, 2009, pp. 468-475.
16. P. Indyk and R. Motwani, "Approximate nearest neighbors: towards removing the curse of dimensionality," in Proc. of 30th ACM Symposium on Theory of Computing, 1998, pp. 604-613.
17. M. Datar, N. Immorlica, P. Indyk, and V. Mirrokni, "Locality- sensitive hashing scheme based on p-stable distributions," in Proceedings of the twentieth annual Symposium on Computational Geometry, 2004, pp. 253-262.
18. W. Liu, J. Wang, S. Kumar, and S.-F. Chang, "Hashing with graphs," in Proceedings of the 28th International Conference on Machine Learning, 2011, pp. 1-8.
19. Y. Weiss, A. Torralba, and R. Fergus, "Spectral hashing," in Proc. of Advances in Neural Information Processing Systems, D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, Eds., 2008, vol. 21, pp. 1753-1760.
20. Y. Gong and S. Lazebnik, "Iterative quantization: A procrustean approach to learning binary codes," in Proc. of IEEE CVPR, 2011, pp. 817-824.
21. B. Kulis and T. Darrell, "Learning to Hash with Binary Reconstructive Embeddings," in Proc. of Advances in Neural Information Processing Systems, Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, Eds., 2009, vol. 20, pp. 1042-1050.
22. W. Liu, J. Wang, R. Ji, Y.-G. Jiang, and S.-F. Chang, "Supervised hashing with kernels," in Proc. of IEEE CVPR, Providence, Rhode Island, USA, 2012.
23. Y. Zhen and D.-Y. Yeung, "A probabilistic model for multimodal hash function learning," in Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012, pp. 940-948.
24. J. Wang, S. Kumar, and S.-F. Chang, "Semi-supervised hashing for large-scale search," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 12, pp. 2393-2406, 2012.
25. P. Jain, S. Vijayanarasimhan, and K. Grauman, "Hashing hyperplane queries to near points with applications to large-scale active learning," in Advances in Neural Information Processing Systems 23, 2010, pp. 928-936.
26. W. Liu, J. Wang, Y. Mu, S. Kumar, and S.-F. Chang, "Compact hyperplane hashing with bilinear functions," in Proceedings of the 28th International Conference on Machine Learning, Edinburgh, Scotland, 2012.
27. Y. Gong, S. Kumar, V. Verma, and S. Lazebnik, "Angular quantizationbased binary codes for fast similarity search," in Advances in Neural Information Processing Systems 25, 2012, pp. 1205-1213.
28. D. Sculley, "Large scale learning to rank," in NIPS 2009 Workshop on Advances in Ranking, Dec 20109.
29. S. Kogan, D. Levin, B. R. Routledge, J. S. Sagi, and N. A. Smith, "Predicting risk from financial reports with regression," in Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, ser. NAACL '09, 2009, pp. 272-280.
30. A. Krizhevsky, "Learning Multiple Layers of Features from Tiny Images," Master's thesis, 2009. [Online]. Available: http://www.cs.toronto. edu/~kriz/learning-features-2009-TR.pdf