Catching the drift: Learning broad matches from clickthrough data

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2009, Pages 1165-1173

  Gupta, Sonal ^a   Bilenko, Mikhail ^b   Richardson, Matthew ^b  

^a University of Texas at Austin   (United States)

^b MICROSOFT RESEARCH   (United States)

Author keywords

Keyword similarity; Keyword based advertising; Online learning

Indexed keywords

CLICKTHROUGH DATA; HIGH QUALITY; IMPLICIT FEEDBACK; KEYWORD SIMILARITY; KEYWORD-BASED ADVERTISING; LEARNING-BASED APPROACH; ONLINE ADVERTISING; ONLINE LEARNING; ONLINE LEARNING ALGORITHMS; PERCEPTRON; SIMILARITY FUNCTIONS; USER BEHAVIORS;

BEHAVIORAL RESEARCH; E-LEARNING; EDUCATION; INTERNET;

LEARNING ALGORITHMS;

EID: 70350666641     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1557019.1557145     Document Type: Conference Paper

References (38)

1. IAB Internet Advertising Revenue Report conducted by PricewaterhouseCoopers (PWC), November 2008. [online] http://www.iab.net/ insights-research/530422/1357.
2. E. Agichtein, E. Brill, and S. Dumais. Improving web search ranking by incorporating user behavior information. In Proceedings of SIGIR-06, pages 19-26, 2006.
3. M. Bilenko and R. J. Mooney. Adaptive duplicate detection using learnable string similarity measures. In Proceedings of KDD-03, pages 39-48, 2003.
4. G. Box, G. M. Jenkins, and G. Reinsel. Time Series Analysis: Forecasting and Control. Wiley, 2008.
5. R. C. Bunescu and M. Pasca. Using encyclopedic knowledge for named entity disambiguation. In Proceedings of EACL-06, 2006.
6. H Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, and H. Li. Context-aware query suggestion by mining click-through and session data. In Proceedings of KDD-08, 2008.
7. B. Carterette and R. Jones. Evaluating search engines by modeling the relationship between relevance and clicks. In Proceedings of NIPS-07, 2007.
8. D. Chakrabarti, D. Agarwal, and V. Josifovski. Contextual advertising by combining relevance with click feedback. In Proceedings of WWW-08, 2008.
9. K. Church and P. Hanks. Word association norms, mutual information and lexicography. In Proceedings of ACL-89, pages 76-83, 1989.
10. M. Ciaramita, V. Murdock, and V. Plachouras. Online learning from click data for sponsored search. In Proceedings of WWW-08, pages 227-236, 2008.
11. W. W. Cohen, P. Ravikumar, and S. E. Fienberg. A comparison of string distance metrics for name-matching tasks. In Proceedings of the IJCAI-03 Workshop on Information Integration on the Web, pages 73-78, 2003.
12. M. Collins. Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. In Proceedings of EMNLP-02, pages 1-8, 2002.
13. S. Cucerzan and E. Brill. Spelling correction as an iterative process that exploits the collective knowledge of web users. In Proceedings of EMNLP-04, pages 293-300, 2004.
14. O. Dekel, S. Shalev-Shwartz, and Y. Singer. The forgetron: A kernel-based perceptron on a budget. SIAM J. Comput., 37(5):1342-1372, 2008.
15. F. Fouss, A. Pirotte, J.-M. Renders, and M. Saerens. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans. Knowl. Data Eng., 19(3):355-369, 2007.
16. Y. Freund and R. E. Schapire. Large margin classification using the perceptron algorithm. In Machine Learning, 1999.
17. A. Fuxman, P. Tsaparas, K. Achan, and R. Agrawal. Using the wisdom of the crowds for keyword generation. In Proceedings of WWW-08, pages 61-70, 2008.
18. Q. Guo and E. Agichtein. Exploring mouse movements for inferring query intent. In Proceedings of SIGIR-08, pages 707-708, 2008.
19. D. Gusfield. Algorithms on Strings, Trees and Sequences. Cambridge University Press, New York, 1997.
20. T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay. Accurately interpreting clickthrough data as implicit feedback. In Proceedings of SIGIR-05, pages 154-161, 2005.
21. T. Joachims and F. Radlinski. Search engines that learn from implicit feedback. IEEE Computer, 40(8):34-40, 2007.
22. R. Jones, B. Rey, O. Madani, and W. Greiner. Generating query substitutions. In Proceedings of WWW-06, pages 387-396, 2006.
23. R. Kohavi, R. M. Henne, and D. Sommerfield. Practical guide to controlled experiments on the web: listen to your customers not to the hippo. In Proceedings of SIGKDD-07, pages 959-967, 2007.
24. R. Kohavi and G. H. John. Wrappers for feature subset selection. Artificial Intelligence, 97(1-2):273-324, 1997.
25. Q. Mei, D. Zhang, and C. Zhai. A general optimization framework for smoothing language models on graph structures. In Proceedings of SIGIR-08, pages 611-618, 2008.
26. D. Metzler, S. T. Dumais, and C. Meek. Similarity measures for short segments of text. In Proceedings of ECIR-07, 2007.
27. J. C. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In A. J. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 185-208. MIT Press, 1999.
28. F. Radlinski, A. Z. Broder, P. Ciccolo, E. Gabrilovich, V. Josifovski, and L. Riedel. Optimizing relevance and revenue in ad search: a query substitution approach. In Proceedings of SIGIR-08, pages 403-410, 2008.
29. M. Regelson and D. Fain. Predicting click-through rate using keyword clusters. In Proceedings of the Second Workshop on Sponsored Search Auctions, 2006.
30. M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: estimating the click-through rate for new ads. In Proceedings of WWW-07, pages 521-530, 2007.
31. M. Sahami and T. D. Heilman. A web-based kernel function for measuring the similarity of short text snippets. In Proceedings of WWW-06, pages 377-386, 2006.
32. S. Sarawagi and A. Bhamidipaty. Interactive deduplication using active learning. In Proceedings of KDD-02, pages 269-278, 2002.
33. V. S. Sheng, F. Provost, and P. G. Ipeirotis. Get another label? improving data quality and data mining using multiple, noisy labelers. In Proceedings of KDD-08, pages 614-622, 2008.
34. E. M. Voorhees. Variations in relevance judgments and the measurement of retrieval effectiveness. In Proceedings of SIGIR-98, pages 315-323, 1998.
35. C. Wang, P. Zhang, and R. Choi. Understanding consumers attitude toward advertising. In Eighth Americas Conference on Information Systems, pages 1143-1148, 2002.
36. R. White, I. Ruthven, and J. M. Jose. The use of implicit evidence for relevance feedback in web retrieval. In Proceedings of ECIR-02, pages 93-109, 2002.
37. W. T. Yih and C. Meek. Improving similarity measures for short segments of text. In Proceedings of AAAI-07, 2007.
38. D. Zhou, S. Zhu, K. Yu, X. Song, B. L. Tseng, H. Zha, and C. L. Giles. Learning multiple graphs for document recommendations. In Proceedings of WWW-08, pages 141-150, 2008.