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    Discovering context-topic rules in search engine logs

    Hurtado, C.A. and Levene, Mark (2006) Discovering context-topic rules in search engine logs. In: Crestani, F. and Ferragina, P. and Sanderson, M. (eds.) International Symposium on String Processing and Information Retrieval. Lecture Notes in Computer Science 4209. Springer, pp. 346-353. ISBN 978-3-540-45774-9.

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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.

    Metadata

    Item Type: Book Section
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 25 May 2021 19:23
    Last Modified: 25 May 2021 19:23
    URI: https://eprints.bbk.ac.uk/id/eprint/44431

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