Predictive Algorithms for Browser Support of Habitual User Activities on the Web

Janez Brank, Natasa Milic-Frayling, Anthony Frayling, and Gavin Smyth

Abstract

Routine activities that users perform on the Web result in the revisitation of sites and pages. Standard browser applications provide limited support for this type of habitual behaviour. They typically expose collections of visited URLs that are automatically recorded by the system, for example the navigation history, or those manually created by the user, such as bookmarks. Studies have shown that these approaches are not very successful in supporting the user in site or page revisitation. Informed by the findings of our user research and analysis of the user navigation logs, we designed SmartFavourites, a browser feature that automatically exposes candidate URLs for revisitation, in a context sensitive manner. In this paper we describe and evaluate the algorithms that we use to model the user’s habitual behaviour. We demonstrate that the use of a structured navigation history model, which essentially captures the domain specific features, facilitates the discovery of relevant usage patterns and predictive algorithms that are applicable to relatively small sizes of personal navigation history.

Details

Publication typeTechReport
NumberMSR-TR-2004-122
Pages11
InstitutionMicrosoft Research
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