Abstract
Most existing search engines employ static ranking algorithms that do not adapt to the specific needs of users. Recently, some researchers have studied the use of clickthrough data to adapt a search engine’s ranking function. Clickthrough data indicate for each query the results that are clicked by users. As a kind of implicit relevance feedback information, clickthrough data can easily be collected by a search engine. However, clickthrough data is sparse and incomplete, thus, it is a challenge to discover accurate user preferences from it. In this paper, we propose a novel algorithm called “Spy Naïve Bayes” (SpyNB) to identify user preferences generated from clickthrough data. First, we treat the result items clicked by the users as sure positive examples and those not clicked by the users as unlabelled data. Then, we plant the sure positive examples (the spies) into the unlabelled set of result items and apply a naïve Bayes classification to generate the reliable negative examples. These positive and negative examples allow us to discover more accurate user’s preferences. Finally, we employ the SpyNB algorithm with a ranking SVM optimizer to build an adaptive metasearch engine. Our experimental results show that, compared with the original ranking, SpyNB can significantly improve the average ranks of users’ click by 20%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bartell, B., Cottrell, G., Belew, R.: Automatic combination of multiple ranked retrieval systemss. In: Proc. of the 17th ACM SIGIR Conference, pp. 173–181 (1994)
Cohen, W., Shapire, R., Singer, Y.: Learning to order things. Journal of Artifical Intelligence Research 10, 243–270 (1999)
Boyan, J., Freitag, D., Joachims, T.: A machine learning architecture for optimizing web search engines. In: Proc. of AAAI workshop on Internet-Based Information System (1996)
Joachims, T.: Optimizing search engines using clickthrough data. In: Proc. of the 8th ACM SIGKDD Conference, pp. 133–142 (2002)
Tan, Q., Chai, X., Ng, W., Lee, D.: Applying co-training to clickthrough data for search engine adaptation. In: Proc. of the 9th DASFAA conference, pp. 519–532 (2004)
Li, X., Liu, B.: Learning to classify text using positive and unlabeled data. In: Proc. of 8th International Joint Conference on Artificial Intelligence (2003)
Liu, B., Dai, Y., Li, X., Lee, W.S.: Building text classifiers using positive and unlabeled examples. In: Proc. of the 3rd International Conference on Data Mining (2003)
Liu, B., Lee, W.S., Yu, P., Li, X.: Partially supervised classification of text documents. In: Proc. of the 19th International Conference on Machine Learning (2002)
Yu, H., Han, J., Chang, K.: PEBL: Positive example based learning for web page classification using svm. In: Proc. of the 8th ACM SIGKDD Conference (2002)
Mitchell, T.: Machine Learning. McGraw Hill, Inc., New York (1997)
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-wesley-Longman, Harlow (1999)
McCallum, A., Nigam, K.: A comparison of event models for naive bayes text classification. In: Proc. of AAAI/ICML 1998 Workshop on Learning for Text Categorization, pp. 41–48 (1998)
Hoffgen, K., Simon, H., Horn, K.V.: Robust trainability of single neurons. Journal of Computer and System Sciences 50, 114–125 (1995)
Joachims, T.: Making large-scale SVM learning practical. In: Scholkoph, B., et al. (eds.) Advances in Kernel Methods – Support Vector Learning. MIT Press, Cambridge (1999), http://svmlight.joachims.org/
Joachims, T.: Evaluating retrieval performance using clickthrough data. In: Proc. of the SIGIR Workshop on Mathematical/Formal Methods in Information Retrieval (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Deng, L., Ng, W., Chai, X., Lee, DL. (2006). Spying Out Accurate User Preferences for Search Engine Adaptation. In: Mobasher, B., Nasraoui, O., Liu, B., Masand, B. (eds) Advances in Web Mining and Web Usage Analysis. WebKDD 2004. Lecture Notes in Computer Science(), vol 3932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11899402_6
Download citation
DOI: https://doi.org/10.1007/11899402_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47127-1
Online ISBN: 978-3-540-47128-8
eBook Packages: Computer ScienceComputer Science (R0)