A Comparison of Content-Based Tag Recommendations in Folksonomy Systems
J. Illig, A. Hotho, R. Jäschke, and G. Stumme. Knowledge Processing and Data Analysis, volume 6581 of Lecture Notes in Computer Science, page 136--149. Berlin/Heidelberg, Springer, (2011)
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.