Ranking function adaptation with boosting trees

ACM Transactions on Information Systems

Volumn 29, Issue 4, 2011, Pages

  Chen, Keke ^a   Bai, Jing ^b   Zheng, Zhaohui ^c  

^a Wright State University   (United States)

^b MICROSOFT   (United States)

^c YAHOO RESEARCH   (United States)

Author keywords

Boosting regression trees; Domain adaptation; Learning to rank; User feedback; Web search ranking

Indexed keywords

DOMAIN ADAPTATION; LEARNING TO RANK; REGRESSION TREES; USER FEEDBACK; WEB SEARCHES;

AUTOMATION; FORESTRY; INFORMATION RETRIEVAL; SEARCH ENGINES; WORLD WIDE WEB;

TREES (MATHEMATICS);

AUTOMATION; FORESTRY; INFORMATION RETRIEVAL; INTERNET; MATHEMATICS; REGRESSION ANALYSIS; TREES;

EID: 84555190553     PISSN: 10468188     EISSN: 15582868     Source Type: Journal    
DOI: 10.1145/2017661.2037663     Document Type: Article

References (61)

1. AGICHTEIN, E., BRILL, E., AND DUMAIS, S. 2006. Improving web search ranking by incorporating user behavior information. In Proceedings of the Annual ACM SIGIR Conference on Research and Development in Information Retrieval.
2. ANDO, R. K. AND ZHANG, T. 2005. A framework for learning predictive structures from multiple tasks and unlabeled data. J. Mach. Learn. Res. 6, 1817-1853.
3. ARGYRIOU, A.,EVGENIOU, T., AND PONTIL, M. 2007. Multi-task feature learning. In Proceedings of the Conference on Advances in Neural Information Processing Systems 19. MIT Press, Cambridge, MA.
4. BACCHIANI, M. AND ROARK, B. 2003. Unsupervised language model adaptation. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP).
5. BAEZA-YATES, R. AND RIBEIRO-NETO, B. 1999. Modern Information Retrieval. Addison Wesley, NY.
6. BEN-DAVID, S., BLITZER, J., CRAMMER, K., AND SOKOLOVA, P. M. 2007. Analysis of representations for domain adaptation. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS). MIT Press, Cambridge, MA.
7. BLITZER, J., CRAMMER, K., KULESZA, A., PEREIRA, O., AND WORTMAN, J. 2008. Learning bounds for domain adaptation. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS). MIT Press, Cambridge, MA.
8. BLITZER, J.,MCDONALD, R., AND PEREIRA, F. 2006. Domain adaptation with structural correspondence learning. In Proceedings of the Conference on Empirical Methods in Natural Language Processing.
9. BURGES, C., LE, Q., AND RAGNO, R. 2006. Learning to rank with nonsmooth cost functions. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS).
10. BURGES, C., SHAKED, T., RENSHAW, E., LAZIER, A., DEEDS, M., HAMILTON, N., AND HULLENDER, G. 2005. Learning to rank using gradient descent. In Proceedings of the International Conference on Machine Learning (ICML).
11. BURGES, C. J. 2010. From ranknet to lambdarank to lambdamart: An overview. Micro. Res. Tech. rep. MSRTR-2010-82.
12. CAO, Y., XU, J., LIU, T.-Y., HUANG, Y., AND HON, H.-W. 2006. Adapting ranking svm to document retrieval. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
13. CAO, Z., QIN, T., LIU, T.-Y., TSAI, M.-F., AND LI, H. 2007. Learning to rank: from pairwise approach to listwise approach. In Proceedings of the 24th International Conference on Machine Learning. ACM, New York, NY, 129-136.
14. CHAPELLE, O. AND ZHANG, Y. 2009. A dynamic Bayesian network click model for web search ranking. In Proceedings of the 18th International Conference on World Wide Web. ACM, New York, NY, 1-10.
15. CHEN, K., LU, R., WONG, C., SUN, G., HECK, L., AND TSENG, B. 2008. Trada: Tree based ranking function adaptation. In Proceedings of the ACM Conference on Information and Knowledge Management.
16. COSSOCK, D. AND ZHANG, T. 2006. Subset ranking using regression. In Proceedings of the ACM Conference on Learning Theory. ACM, New York, NY, 605-619. (Pubitemid 44072222)
17. CRISTIANINI, N. AND SHAWE-TAYLOR, J. 2000. An Introduction to Support Vector Machines and Other Kernel- Based Learning Methods. Cambridge University Press, UK.
18. DAI, W., YANG, Q., XUE, G.-R., AND YU, Y. 2007. Boosting for transfer learning. In Proceedings of the 24th International Conference on Machine Learning. ACM, New York, NY, 193-200.
19. DAI, W., YANG, Q., XUE, G.-R., AND YU, Y. 2008. Self-taught clustering. In Proceedings of the 25th International Conference on Machine Learning. ACM, New York, NY, 200-207.
20. DAUMÉ III, H. AND MARCU, D. 2006. Domain adaptation for statistical classifiers. J. Mach. Learn. Res. 16, 1817-1853.
21. DONG, A., CHANG, Y., JI, S., LIAO, C., LI, X., AND ZHENG, Z. 2009. Empirical exploitation of click data for task specific ranking. In Proceedings of the Conference on EmpiricalMethods in Natural Language Processing. Association for Computational Linguistics, Morristown, NJ, 1086-1095.
22. EVGENIOU, T. AND PONTIL, M. 2004. Regularized multitask learning. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, 109-117.
23. FAN, R.-E., CHANG, K.-W., HSIEH, C.-J., WANG, X.-R., AND LIN, C.-J. 2008. Liblinear: A library for large linear classification. J. Mach. Learn. Res. 9, 1871-1874.
24. FREUND, Y., IYER, R., SCHAPIRE, R. E., AND SINGER, Y. 2003. An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4, 933-969.
25. FREUND, Y. AND SCHAPIRE, R. E. 1999. A short introduction to boosting. In Proceedings of the 16th International Joint Conference on Artificial Intelligence. Morgan Kaufmann, 1401-1406.
26. FRIEDMAN, J. H. 2001. Greedy function approximation: A gradient boosting machine. Annal. Stat. 29, 5, 1189-1232. (Pubitemid 33405972)
27. FRIEDMAN, J. H. AND POPESCU, B. E. 2003. Importance sampled learning ensembles. J. Mach. Learn. Res. 9, 4305.
28. GAO, J., WU, Q., BURGES, C., SVORE, K., SU, Y., KHAN, N., SHAH, S., AND ZHOU, H. 2009. Model adaptation via model interpolation and boosting for web search ranking. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Singapore, 505-513.
29. GENG, B., YANG, L., XU, C., AND HUA, X.-S. 2009. Ranking model adaptation for domain-specific search. In Proceedings of the 18th ACM Conference on Information and Knowledge Management. ACM, New York, NY, 197-206.
30. GEURTS, P. 2010. Learning to rank with extremely randomized regression trees. In Proceedings of Yahoo! Learning to Rank Challenge Workshop in ICML.
31. GULIN, A. AND KURALENOK, I. 2010. Yetirank: Everybody lies. In Yahoo! Learning to Rank Challenge Workshop in ICML.
32. HASTIE, T., TIBSHIRANI, R., AND FRIEDMAN, J. 2001. The Elements of Statistical Learning. Springer-Verlag.
33. HERBRICH, R., GRAEPEL, T., AND OBERMAYER, K. 2000. Large margin rank boundaries for ordinal regression. Adv. Large Marg. Class. 115-132.
34. HWA, R. 1999. Supervised grammar induction using training data with limited constituent information. In Proceedings of the Conference of the Association for Computational Linguistics.
35. JARVELIN, K. AND KEKALAINEN, J. 2000. IR evaluation methods for retrieving highly relevant documents. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval.
36. JI, S., ZHOU, K., LIAO, C., ZHENG, Z., XUE, G.-R., CHAPELLE, O., SUN, G., AND ZHA, H. 2009. Global ranking by exploiting user clicks. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, NY, 35-42.
37. JIANG, J. AND ZHAI, C. 2007. Instance weighting for domain adaptation inNLP. In Proceedings of the Conference of the Association for Computational Linguistics.
38. JOACHIMS, T. 2002. Optimizing search engines using clickthrough data. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
39. JOACHIMS, T., GRANKA, L., PAN, B., AND GAY, G. 2005. Accurately interpreting clickthrough data as implicit feedback. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval.
40. LEGGETTER, C. AND WOODLAND, P. 1995. Flexible speaker adaptation using maximum likelihood linear regression. In Proceedings of Eurospeech.
41. LEHMANN, E. L. AND CASELLA, G. 1998. Theory of Point Estimation. Springer-Verlag.
42. LI, P., BURGES, C. J., AND WU, Q. 2007. Mcrank: Learning to rank using multiple classification and gradient boosting. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS).
43. LIAO, X., XUE, Y., AND CARIN, L. 2005. Logistic regression with an auxiliary data source. In Proceedings of the 22nd International Conference on Machine Learning. ACM, New York, NY, 505-512. (Pubitemid 43183372)
44. LONG, B., LAMKHEDE, S., VADREVU, S., ZHANG, Y., AND TSENG, B. L. 2009. A risk minimization framework for domain adaptation. In Proceedings of the Conference on Information and KnowledgeManagement, ACM, New York, NY, 1347-1356.
45. MANSOUR, Y., MOHRI, M., AND ROSTAMIZADEH, A. 2009. Domain adaptation: Learning bounds and algorithms. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS).
46. MOHAN, A.,CHEN, Z., ANDWEINBERGER, K. 2010. Tree ensemble and transfer learning. In Proceedings of Yahoo! Learning to Rank Challenge Workshop in ICML.
47. NALLAPATI, R. 2004. Discriminative models for information retrieval. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, New York, NY, 64-71.
48. PAN, S. J. AND YANG, Q. 2010. A survey on transfer learning. IEEE Trans. Knowl. Data Engin. 22, 10, 1345-1359.
49. PAVLOV, D. AND BRUNK, C. 2010. Bagboo: Bagging the gradient boosting. In Proceedings of Yahoo! Learning to Rank Challenge Workshop in ICML.
50. RAINA, R., BATTLE, A., LEE, H., PACKER, B., AND NG, A. Y. 2007. Self-taught learning: transfer learning from unlabeled data. In Proceedings of the 24th International Conference on Machine Learning. ACM, New York, NY, 759-766.
51. SCHAPIRE, R. E. 2003. The boosting approach to machine learning: An overview. Nonlinear Estimation and Classification.
52. SOROKINA, D. 2010. Application of additive groves to the learning to rank challenge. In Proceedings of Yahoo! Learning to Rank Challenge Workshop in ICML.
53. TSAI, M.-F., LIU, T.-Y., QIN, T., CHEN, H.-H., AND MA, W.-Y. 2007. Frank: a ranking method with fidelity loss. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, New York, NY, 383-390. (Pubitemid 350164984)
54. VAPNIK, V. N. 1999. The Nature of Statistical Learning Theory. Springer Science and Business Media, LLC, Berlin, Germany.
55. WANG, Z., SONG, Y., AND ZHANG, C. 2008. Transferred dimensionality reduction. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases. Springer-Verlag, Berlin, Germany, 550-565.
56. WU, P. AND DIETTERICH, T. G. 2004. Improving svm accuracy by training on auxiliary data sources. In Proceedings of International Conference on Machine Learning. 871-878. (Pubitemid 40290892)
57. WU, Q.,BURGES, C. J., SVORE, K., AND GAO, J. 2008. Ranking, boosting, and model adaptation. Micro. Res. Tech. rep., Microsoft.
58. WU, Q.,BURGES, C. J. C., SVORE, K. M., AND GAO, J. 2010. Adapting boosting for information retrievalmeasures. Inf. Retr. 13, 3, 254-270.
59. XU, J. AND LI, H. 2007. AdaRank: a boosting algorithm for information retrieval. In Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval.
60. ZHENG, Z.,CHEN, K., SUN, G., AND ZHA, H. 2007a. A regression framework for learning ranking functions using relative relevance judgments. In Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, NY, 287-294. (Pubitemid 350167353)
61. ZHENG, Z., ZHA, H., ZHANG, T., CHAPELLE, O., CHEN, K., AND SUN, G. 2007b. A general boosting method and its application to learning ranking functions for web search. In Proceedings Of Neural Information Processing Systems (NIPS).