Using behavioral data mining to produce friend recommendations in a social bookmarking system

Social recommender systems have been developed to filter the large amounts of data generated by social media systems. A type of social media, known as social bookmarking system, allows the users to tag bookmarks of interest and to share them. Although the popularity of these systems is increasing and even if users are allowed to connect both by following other users or by adding them as friends, no friend recommender system has been proposed in the literature. Behavioral data mining is a useful tool to extract information by analyzing the behavior of the users in a system. In this paper we first perform a preliminary analysis that shows that behavioral data mining is effective to discover how similar the preferences of two users are. Then, we exploit the analysis of the user behavior to produce friend recommendations, by analyzing the resources tagged by a user and the frequency of each used tag. Experimental results highlight that, by analyzing both the tagging and bookmarking behaviors of a user, our approach is able to mine preferences in a more accurate way with respect to a state-of-the-art approach that considers only the tags.