Birds of a feather surf together: using clustering methods to improve navigation prediction from internet log files

Martin Halvey, Mark T. Keane, Barry Smyth

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Many systems attempt to forecast user navigation in the Internet through the use of past behavior, preferences and environmental factors. Most of these models overlook the possibility that users may have many diverse sets of preferences. For example, the same person may search for information in different ways at night (when they are pursuing their hobbies and interests) as opposed to during the day (when they are at work). Thus, most users may well have different sets of preferences at different times of the day and behave differently in accordance with those preferences. In this paper, we present clustering methods for creating time dependent models to predict user navigation patterns; these methods allow us to segment log files into appropriate groups of navigation behaviour. The benefits of these methods over more established methods are highlighted. An empirical analysis is carried out on a sample of usage logs for Wireless Application Protocol (WAP) browsing as empirical support for the technique.
    Original languageEnglish
    Title of host publicationMachine learning and data mining in pattern recognition
    PublisherSpringer
    Pages174-183
    Number of pages10
    ISBN (Electronic)9783540318910
    ISBN (Print)9783540269236
    DOIs
    Publication statusPublished - 2005

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer
    Volume3587
    ISSN (Print)0302-9743

    Keywords

    • WAP
    • internet log files
    • user navigation
    • user preferences
    • clustering methods

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  • Cite this

    Halvey, M., Keane, M. T., & Smyth, B. (2005). Birds of a feather surf together: using clustering methods to improve navigation prediction from internet log files. In Machine learning and data mining in pattern recognition (pp. 174-183). (Lecture Notes in Computer Science; Vol. 3587). Springer. https://doi.org/10.1007/11510888_18