A comparison of content-based tag recommendations in folksonomy systems

Authors:
Jens Illig;Andreas Hotho;Robert Jäschke;Gerd Stumme
Affiliations:
Knowledge & Data Engineering Group, Department of Mathematics and Computer Science, University of Kassel, Kassel, Germany;Knowledge & Data Engineering Group, Department of Mathematics and Computer Science, University of Kassel, Kassel, Germany;Knowledge & Data Engineering Group, Department of Mathematics and Computer Science, University of Kassel, Kassel, Germany and Research Center L3S, Hannover, Germany;Knowledge & Data Engineering Group, Department of Mathematics and Computer Science, University of Kassel, Kassel, Germany and Research Center L3S, Hannover, Germany
Venue:
KONT'07/KPP'07 Proceedings of the First international conference on Knowledge processing and data analysis
Year:
2007

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Abstract

Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i. e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset.