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 proceeding › Chapter (peer-reviewed)

    Abstract

    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
    PublisherACM
    Pages958-959
    Number of pages1
    ISBN (Print)1595930515
    DOIs
    Publication statusPublished - 2005

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    Navigation
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    Keywords

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

    Cite this

    Halvey, M., Keane, M. T., & Smyth, B. (2005). Predicting navigation patterns on the mobile-internet using time of the week. In WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web (pp. 958-959). New York, USA: ACM. https://doi.org/10.1145/1062745.1062817
    Halvey, Martin ; Keane, Mark T. ; Smyth, Barry. / Predicting navigation patterns on the mobile-internet using time of the week. WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web. New York, USA : ACM, 2005. pp. 958-959
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    abstract = "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.",
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    author = "Martin Halvey and Keane, {Mark T.} and Barry Smyth",
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    Halvey, M, Keane, MT & Smyth, B 2005, Predicting navigation patterns on the mobile-internet using time of the week. in WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web. ACM, New York, USA, pp. 958-959. https://doi.org/10.1145/1062745.1062817

    Predicting navigation patterns on the mobile-internet using time of the week. / Halvey, Martin; Keane, Mark T.; Smyth, Barry.

    WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web. New York, USA : ACM, 2005. p. 958-959.

    Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed)

    TY - CHAP

    T1 - Predicting navigation patterns on the mobile-internet using time of the week

    AU - Halvey, Martin

    AU - Keane, Mark T.

    AU - Smyth, Barry

    PY - 2005

    Y1 - 2005

    N2 - 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.

    AB - 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.

    KW - mobile-internet

    KW - user navigation evaluation

    KW - data use

    KW - surfing patterns

    KW - predictive accuracy

    U2 - 10.1145/1062745.1062817

    DO - 10.1145/1062745.1062817

    M3 - Chapter (peer-reviewed)

    SN - 1595930515

    SP - 958

    EP - 959

    BT - WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web

    PB - ACM

    CY - New York, USA

    ER -

    Halvey M, Keane MT, Smyth B. Predicting navigation patterns on the mobile-internet using time of the week. In WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web. New York, USA: ACM. 2005. p. 958-959 https://doi.org/10.1145/1062745.1062817

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