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    Evaluating variable-length Markov chain models for analysis of user web navigation sessions

    de Moura Borges, J.L.C. and Levene, Mark (2007) Evaluating variable-length Markov chain models for analysis of user web navigation sessions. IEEE Transactions on Knowledge and Data Engineering 19 (4), pp. 441-452. ISSN 1041-4347.

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    Abstract

    Markov models have been widely used to represent and analyze user Web navigation data. In previous work, we have proposed a method to dynamically extend the order of a Markov chain model and a complimentary method for assessing the predictive power of such a variable-length Markov chain. Herein, we review these two methods and propose a novel method for measuring the ability of a variable-length Markov model to summarize user Web navigation sessions up to a given length. Although the summarization ability of a model is important to enable the identification of user navigation patterns, the ability to make predictions is important in order to foresee the next link choice of a user after following a given trail so as, for example, to personalize a Web site. We present an extensive experimental evaluation providing strong evidence that prediction accuracy increases linearly with summarization ability.

    Metadata

    Item Type: Article
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 25 May 2021 18:11
    Last Modified: 25 May 2021 18:11
    URI: https://eprints.bbk.ac.uk/id/eprint/44421

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