Predictive algorithms for browser support of habitual user activities on the Web

Abstract

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 user's 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.