Predictive Algorithms for Browser Support of Habitual User Activities on the Web
Routine user activities on the Web result in the revisitation of Web sites and pages. Standard browser applications provide limited support for this type of habitual behaviour. They typically expose lists of visited URLs that are automatically recorded by the system or manually created by the user,...
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Published in | Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence pp. 629 - 635 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
Washington, DC, USA
IEEE Computer Society
19.09.2005
IEEE |
Series | ACM Conferences |
Subjects | |
Online Access | Get full text |
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Summary: | Routine user activities on the Web result in the revisitation of Web sites and pages. Standard browser applications provide limited support for this type of habitual behaviour. They typically expose lists of visited URLs that are automatically recorded by the system or manually created by the user, such as bookmarks. Studies have shown that these approaches are not successful in supporting routine user activities. Informed by our user research we designed a browser feature that automatically exposes candidate URLs for revisitation by the user. In this paper we describe and evaluate the algorithms that we use to model the userýs habitual behaviour. We demonstrate how a structured navigation history model facilitates the discovery of relevant usage patterns and supports predictive algorithms that are applicable to relatively short personal navigation histories. |
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Bibliography: | SourceType-Conference Papers & Proceedings-1 ObjectType-Conference Paper-1 content type line 25 |
ISBN: | 076952415X 9780769524153 |
DOI: | 10.1109/WI.2005.116 |