In this paper, we present a class of rules, called context-topic rules, for discovering associations between topics and contexts, where a context is defined as a set of features that can be extracted from the log file of a Web search engine. We introduce a notion of rule interestingness that measures the level of the interest of the topic within a context, and provide an algorithm to compute concise representations of interesting context-topic rules. Finally, we present the results of applying the methodology proposed to a large data log of a search engine. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Hurtado, C. A., & Levene, M. (2006). Discovering context-topic rules in search engine logs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4209 LNCS, pp. 346–353). Springer Verlag. https://doi.org/10.1007/11880561_29
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