%0 Conference Paper
%1 illig2009comparison
%A Illig, Jens
%A Hotho, Andreas
%A Jäschke, Robert
%A Stumme, Gerd
%B Knowledge Processing and Data Analysis
%C Berlin/Heidelberg
%D 2011
%E Wolff, Karl Erich
%E Palchunov, Dmitry E.
%E Zagoruiko, Nikolay G.
%E Andelfinger, Urs
%I Springer
%K 2011 content folksonomy myown recommendations recommender tag
%P 136--149
%T A Comparison of Content-Based Tag Recommendations in Folksonomy Systems
%U http://dx.doi.org/10.1007/978-3-642-22140-8_9
%V 6581
%X 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.