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, Springer Berlin / Heidelberg, 10.1007/978-3-642-22140-8_9.(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.