Supporting collaborative hierarchical classification: Bookmarks as an example

Authors:
Dominik Benz;Karen H. L. Tso;Lars Schmidt-Thieme
Affiliations:
Computer-based New Media Group (CGNM), Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 51, 79110 Freiburg, Germany;Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Samelsonplatz 1, 31141 Hildesheim, Germany;Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Samelsonplatz 1, 31141 Hildesheim, Germany
Venue:
Computer Networks: The International Journal of Computer and Telecommunications Networking
Year:
2007

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Abstract

Bookmarks (or favorites, hotlists) are popular strategies to relocate interesting websites on the WWW by creating a personalized URL repository. Most current browsers offer a facility to locally store and manage bookmarks in a hierarchy of folders; though, with growing size, users reportedly have trouble to create and maintain a stable organization structure. This paper presents a novel collaborative approach to ease bookmark management, especially the ''classification'' of new bookmarks into a folder. We propose a methodology to realize the collaborative classification idea of considering how similar users have classified a bookmark. A combination of nearest-neighbor-classifiers is used to derive a recommendation from similar users on where to store a new bookmark. A prototype system called CariBo has been implemented as a plugin for the central bookmark server software SiteBar. All findings have been evaluated on a reasonably large scale, real user dataset with promising results, and possible implications for shared and social bookmarking systems are discussed.