Online learning from click data for sponsored search

Proceeding of the 17th International Conference on World Wide Web 2008, WWW'08

Volumn , Issue , 2008, Pages 227-236

  Ciaramita, Massimiliano ^a   Murdock, Vanessa ^a   Plachouras, Vassilis ^a  

^a YAHOO RESEARCH   (Spain)

Author keywords

Online learning; Perceptrons; Ranking; Sponsored search

Indexed keywords

COMPUTER SOFTWARE; E-LEARNING; EDUCATION; INFORMATION RETRIEVAL; INFORMATION SERVICES; INTERNET; LEARNING SYSTEMS; NEURAL NETWORKS; PATTERN RECOGNITION SYSTEMS; SEARCH ENGINES; TELECOMMUNICATION NETWORKS;

BASE-LINES; CONTENT SIMILARITIES; DATA ENCODING; ENABLING TECHNOLOGIES; LEARNING FRAMEWORKS; LEARNING MODELS; MACHINE LEARNINGS; MULTILAYER PERCEPTRON; ON-LINE LEARNINGS; ONLINE LEARNING; PERCEPTRONS; RANKING; SEARCH PROBLEMS; SEARCH TASKS; SPONSORED SEARCH; USER QUERIES; WEB SEARCH ENGINES;

WORLD WIDE WEB;

EID: 57349195514     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1367497.1367529     Document Type: Conference Paper

References (35)

1. E. Agichtein, E. Brill, and S. Dumais. Improving web search ranking by incorporating user behavior information. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, 2006.
2. A. Broder, M. Fontoura, V. Josifovski, and L. Riedel. A semantic approach to contextual advertising. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 559-566. ACM Press, 2007.
3. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In Proceedings of the 22nd International Conference on Machine Learning (ICML), pages 89-96, 2005.
4. J. Carrasco, D. Fain, K. Lang, and L. Zhukov. Clustering of bipartite advertiser-keyword graph. In Proceedings of the Workshop on Clustering Large Datasets, IEEE Conference on Data Mining. IEEE Computer Society Press, 2003.
5. G. Cauwenberghs and T. Poggio. Incremental and decremental support vector machine learning. In Advances in Neural Information Processing Systems, pages 409-415, 2000.
6. M. Ciaramita, V. Murdock, and V. Plachouras. Semantic associations for contextual advertising. International Journal of Electronic Commerce Research - Special Issue on Online Advertising and Sponsored Search, 9(1), pages 1-15, 2008.
7. M. Collins and B. Roark. Incremental parsing with the perceptron algorithm. In Proceedings of ACL 2004, 2004.
8. R. Duda, P. Hart, and D. Stork. Pattern Classification (2nd ed). Wiley-Interscience, 2000.
9. J. Feng, H. Bhargava, and D. Pennock. Implementing sponsored search in web search engines: Computational evaluation of alternative mechanisms. INFORMS Journal on Computing, 19(1): 137-148, 2007.
10. Y. Freund and R. Schapire. Large margin classification using the perceptron algorithm. Machine Learning, 37(3):277-296, 1999.
11. L. Granka, T. Joachims, and G. Gay. Eye-tracking analysis of user behavior in WWW search. In ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pages 478-479, 2004.
12. IAB. Internet advertising revenue report: 2006 full-year results, 2007. http://www.iab.net/resources/.
13. T. Joachims. Optimizing search engines using clickthrough data. In Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD). ACM, 2002.
14. T. Joachims, L. Granka, B. Pang, H. Hembrooke, and G. Gay. Accurately interpreting clickthrough data as implicit feedback. In ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pages 154-161, 2005.
15. R. Jones, B. Rey, O. Madani, and W. Greiner. Generating query substitutions. In Proceedings of the 15th International World Wide Web Conference (WWW), 2006.
16. D. Kelly. Implicit feedback: Using behavior to infer relevance. In New Directions in Cognitive Information Retrieval, pages 169 - 186. Springer Publishing, 2005.
17. J. Kivinen, A. Smola, and R. Williamson. Online learning with kernels. In Advances in Neural Information Processing Systems, pages 785-792, 2001.
18. R. Krovetz. Viewing morphology as an inference process. In Proceedings of the 16th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1993.
19. A. Lacerda, M. Cristo, M. Goncalves, W. Fan, N. Ziviani, and B. Ribeiro-Neto. Learning to advertise. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 549-556. ACM Press, 2006.
20. Y. Li, H. Zaragoza, R. Herbrich, J. Shawe-Taylor, and J. Kandola. The perceptron algorithm with uneven margins. In Proceedings of the Nineteenth International Conference on Machine Learning (ICML), pages 379-386, San Francisco, CA, USA, 2002. Morgan Kaufmann Publishers Inc.
21. T. Liu, J, Xu, T. Qin, W. Xiong, and H. Li. Letor: Benchmark dataset for research on learning to rank for information retrieval. In Proceedings of the SIGIR 2007 Workshop on Learning to Rank for Information Retrieval, 2007.
22. M. Minsky and S. Papert. Perceptrons. MIT Press, Cambridge, MA, 1969.
23. V. Murdock, M. Ciaramita, and V. Plachouras. A noisy channel approach to contextual advertising. In Proceedings of the 1st International Workshop on Data Mining and Audience Intelligence for Advertising (ADKDD'07), pages 21-27, 2007.
24. One Up Web. How keyword length affects conversion rates, 2005. http://www.oneupweb.com/landing/keywordstudy-landing.htm.
25. B. Ribeiro-Neto, M. Cristo, P. Golgher, and E. D. Moura. Impedance coupling in content-targeted advertising. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 496-503. ACM Press, 2005.
26. F. Rosemblatt. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6):386-408, 1958.
27. D. Rumelhart, G. Hinton, and R. Williams. Learning internal representation by backpropagating errors. Nature, 323(99):533-536, 1986.
28. M. Sahami and T. D. Heilman. A web-based kernel function for measuring the similarity of short text snippets. In Proceedings of the 15th international conference on World Wide Web, pages 377-386, New York, NY, USA, 2006. ACM.
29. F. Sha and F. Pereira. Shallow parsing with conditional random fields. In Proceedings of Human Language Technology and North-American Chapter of the Association for Computational Linguistics (HLT-NAACL), 2003.
30. L. Shen and A. Joshi. Ranking and reranking with perceptron. Machine Learning. Special Issue on Learning in Speech and Language Technologies, 60(1-3):73-96, 2005.
31. M. Surdeanu and M. Ciaramita. Robust information extraction with perceptrons. In Proceedings of NIST Automatic Content Extraction Workshop (ACE), 2007.
32. G.-R. Xue, H.-J. Zeng, Z. Chen, Y. Yu, W.-Y. Ma, W. Xi, and W. Fan. Optimizing web search using web click-through data. In Proceedings of the thirteenth ACM international conference on Information and knowledge 'management (CIKM), pages 118-126, New York, NY, USA, 2004. ACM.
33. W. Yih, J. Goodman, and V. Carvalho. Finding advertising keywords on web pages. In Proceedings of the 15th international conference on World Wide Web, pages 213-222, 2006.
34. C. Y. Yoo. Preattentive Processing of Web Advertising. PhD thesis, University of Texas at Austin, 2006.
35. W. V. Zhang, X. He, B. Rey, and R. Jones. Query rewriting using active learning for sponsored search. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007.