Abstract
Query logs record past query sessions across a time span. A statistical model is proposed to explain the log generation process. Within a search engine list of results, the model explains the document selection – a user’s click – by taking into account both a document position and its popularity. We show that it is possible to quantify this influence and consequently estimate document “un-biased” popularities. Among other applications, this allows to re-order the result list to match more closely user preferences and to use the logs as a feedback to improve search engines.
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Dupret, G., Piwowarski, B., Hurtado, C., Mendoza, M. (2006). A Statistical Model of Query Log Generation. In: Crestani, F., Ferragina, P., Sanderson, M. (eds) String Processing and Information Retrieval. SPIRE 2006. Lecture Notes in Computer Science, vol 4209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11880561_18
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DOI: https://doi.org/10.1007/11880561_18
Publisher Name: Springer, Berlin, Heidelberg
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