IntRank: Interaction Ranking-Based Trustworthy Friend Recommendation

Social networks are fundamental to virtual communities (e.g., forums, blogs) and virtual communities benefit from well-established social networks. As making friends with other members is a common way to establish social relationships and people need to decide whom they should trust when making friends, friend recommendation has received considerable attention in virtual communities. Towards this goal, we first formulate hypotheses on factors that influence trust and the probability of establishing friendships from various interaction attributes in virtual communities. Through experiments on real interaction and friendship data, we validate the proposed hypotheses and propose a novel interaction ranking-based trustworthy friend recommendation model called IntRank for recommending trustworthy friends to community members. Different from traditional friend recommendation mechanisms, IntRank is built on the foundation of carefully verified interaction attributes that influence trust and friendship probability in virtual communities. It is able to effectively recommend trustworthy friends as confirmed by the performance evaluation results.