Discovering context-topic rules in search engine logs

Authors:
Carlos A. Hurtado;Mark Levene
Affiliations:
Universidad de Chile;Birkbeck, University of London
Venue:
SPIRE'06 Proceedings of the 13th international conference on String Processing and Information Retrieval
Year:
2006

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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 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.