A comparison of content-based tag recommendations in folksonomy systems

Abstract. Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Con-tent based recommenders are the usual approach for facing the cold start prob-lem, 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.