Predicting navigation patterns on the mobile-internet using time of the week

Martin Halvey, Mark T. Keane, Barry Smyth

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

    44 Citations (Scopus)


    A predictive analysis of user navigation in the Internet is presented that exploits time-of-the-week data. Specifically, we investigate time as an environmental factor in making predictions about user navigation. An analysis is carried out of a large sample of user, navigation data (over 3.7 million sessions from 0.5 million users) in a mobile-Internet context to determine whether user surfing patterns vary depending on the time of the week on which they occur. We find that the use of time improves the predictive accuracy of navigation models.
    Original languageEnglish
    Title of host publicationWWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
    Place of PublicationNew York, USA
    PublisherAssociation for Computing Machinery (ACM)
    Number of pages1
    ISBN (Print)1595930515
    Publication statusPublished - 2005


    • mobile-internet
    • user navigation evaluation
    • data use
    • surfing patterns
    • predictive accuracy


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