International Conference on Data Management Technologies and Applications

Data Management Technologies and Applications pp 99-116 | Cite as

Using Behavioral Data Mining to Produce Friend Recommendations in a Social Bookmarking System

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 178)


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.


Social bookmarking Friend recommendation Behavioral data mining Tagging system 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Matteo Manca
    • 1
  • Ludovico Boratto
    • 1
  • Salvatore Carta
    • 1
  1. 1.Dipartimento di Matematica e InformaticaUniversità di CagliariCagliariItaly

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