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

Martin Halvey; Mark T. Keane; Barry Smyth

doi:10.1145/1062745.1062817

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) › peer-review

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 language	English
Title of host publication	WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Place of Publication	New York, USA
Publisher	ACM
Pages	958-959
Number of pages	1
ISBN (Print)	1595930515
DOIs	https://doi.org/10.1145/1062745.1062817
Publication status	Published - 2005

Keywords

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

Documents and Links

10.1145/1062745.1062817

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title = "Predicting navigation patterns on the mobile-internet using time of the week",

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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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

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DO - 10.1145/1062745.1062817

M3 - Chapter (peer-reviewed)

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