Clustering of Social Tagging System Users: A Topic and Time Based Approach

Authors:
Vassiliki Koutsonikola;Athena Vakali;Eirini Giannakidou;Ioannis Kompatsiaris
Affiliations:
Department of Informatics, Aristotle University, Thessaloniki, Greece 54124;Department of Informatics, Aristotle University, Thessaloniki, Greece 54124;Department of Informatics, Aristotle University, Thessaloniki, Greece 54124 and Informatics and Telematics Institute, CERTH, Thermi-Thessaloniki, Greece;Informatics and Telematics Institute, CERTH, Thermi-Thessaloniki, Greece
Venue:
WISE '09 Proceedings of the 10th International Conference on Web Information Systems Engineering
Year:
2009

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Abstract

Under Social Tagging Systems, a typical Web 2.0 application, users label digital data sources by using freely chosen textual descriptions (tags). Mining tag information reveals the topic-domain of users interests and significantly contributes in a profile construction process. In this paper we propose a clustering framework which groups users according to their preferred topics and the time locality of their tagging activity. Experimental results demonstrate the efficiency of the proposed approach which results in more enriched time-aware users profiles.