Abstract
There are numerous queries for which search engine results are not satisfactory. For instance, the user may submit an ambiguous or miss-spelled query; or there might be a mismatch between query and document vocabulary, or even character set in some languages. Different automatic methods for query rewriting / refinement have been proposed in the literature, but little work has been done on how to combine the results of these rewrites to find relevant documents. In this paper, we review some techniques efficient enough to be computed online and we discuss their respective assumptions. We also propose and discuss a new model that is theoretically more appealing while still computationally very efficient. Our experiments show that all methods manage to improve the ranking of a leading commercial search engine.
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.
Similar content being viewed by others
References
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Boldi, P., Bonchi, F., Castillo, C., Donato, D., Gionis, A., Vigna, S.: The query-flow graph: model and applications. In: CIKM 2008: Proceeding of the 17th ACM conference on Information and knowledge mining, pp. 609–618. ACM, New York (2008)
Buehrer, G., Stokes, J.W., Chellapilla, K.: A large-scale study of automated web search traffic. In: AIRWeb 2008: Proceedings of the 4th international workshop on Adversarial information retrieval on the web, pp. 1–8. ACM, New York (2008)
Chapelle, O., Zhang, Y.: A dynamic bayesian network click model for web search ranking. In: WWW 2009: Proceedings of the 18th international conference on World wide web, pp. 1–10. ACM, New York (2009)
Craswell, N., Szummer, M.: Random walks on the click graph. In: SIGIR 2007: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 239–246. ACM Press, New York (2007)
Craswell, N., Zoeter, O., Taylor, M., Ramsey, B.: An experimental comparison of click position-bias models. In: First ACM International Conference on Web Search and Data Mining WSDM 2008 (2008)
Dupret, G., Mendoza, M.: Recommending better queries based on click-through data. In: Apostolico, A., Melucci, M. (eds.) SPIRE 2004. LNCS, vol. 3246, pp. 41–44. Springer, Heidelberg (2004)
Dupret, G., Piwowarski, B.: A user browsing model to predict search engine click data from past observations. In: Press, A. (ed.) Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (2008)
Guo, F., Liu, C., Kannan, A., Minka, T., Taylor, M., Wang, Y.M., Faloutsos, C.: Click chain model in web search. In: WWW 2009: Proceedings of the 18th international conference on World wide web, pp. 11–20. ACM, New York (2009)
Hofmann, T.: Probabilistic Latent Semantic Indexing. In: Proceedings of the 22nd Annual ACM Conference on Research and Development in Information Retrieval, pp. 50–57. Berkeley, California (August 1999)
Joachims, T.: Optimizing search engines using clickthrough data. In: KDD 2002: Proceedings of the eighth ACM SIGKDD, pp. 133–142. ACM Press, New York (2002)
Jones, R., Fain, D.C.: Query word deletion prediction. In: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in information retrieval (2003)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems (ACM TOIS) 20(4), 222–246 (2002)
Radlinski, F., Joachims, T.: Active exploration for learning rankings from clickthrough data. In: Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD). ACM, New York (2007)
Robertson, S.E.: The probability ranking principle in ir. Journal of Documentation- 33(4), 294–304 (1977)
Velez, B., Weiss, R., Sheldon, M.A., Gifford, D.K.: Fast and effective query refinement. In: Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dupret, G., Zilleruelo-Ramos, R., Fujita, S. (2010). Using Related Queries to Improve Web Search Results Ranking. In: Chavez, E., Lonardi, S. (eds) String Processing and Information Retrieval. SPIRE 2010. Lecture Notes in Computer Science, vol 6393. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16321-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-16321-0_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16320-3
Online ISBN: 978-3-642-16321-0
eBook Packages: Computer ScienceComputer Science (R0)