Social networks and interest similarity: the case of CiteULike

ABSTRACT

In collaborative filtering recommender systems, there is little room for users to get involved in the choice of their peer group. It leaves users defenseless against various spamming or ''shilling'' attacks. Other social Web-based systems, however, allow users to self-select peers and build a social network. We argue that users' self-defined social networks could be valuable to increase the quality of recommendation in CF systems. To prove the feasibility of this idea we examined how similar are interests of users connected by self-defined relationships in a collaborative tagging systems Citeulike. Interest similarity was measured by similarity of items and meta-data they share and tags they use. Our study shows that users connected by social networks exhibit significantly higher similarity on all explored levels (items, meta-data, and tags) than non-connected users. This similarity is the highest for directly connected users and decreases with the increase of distance between users. Among other interesting properties of information sharing is the finding that between-user similarity in social connections on the level of metadata and tags is much larger than similarity on the level of items. Overall, our findings support the feasibility of social network based recommender systems and offer some good hints to the prospective authors of these systems.

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Index Terms

• Applied computing

  • Law, social and behavioral sciences

    • Sociology

• Computing methodologies

  • Artificial intelligence

    • Philosophical/theoretical foundations of artificial intelligence

      • Cognitive science

• Human-centered computing

  • Human computer interaction (HCI)

    • Empirical studies in HCI