Discovering context-topic rules in search engine logs
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Abstract
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 interesting-ness 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.
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STRING PROCESSING AND INFORMATION RETRIEVAL, PROCEEDINGS Book Series: LECTURE NOTES IN COMPUTER SCIENCE Volume: 4209 Pages: 346-353 Published: 2006
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