Information fusion oriented heterogeneous social network for friend recommendation via community detection

Abstract

The rapid advance of online social networks and the tremendous growth in the number of participants and attention have led to information overload and increased the difficulty of making accurate recommendations of new friends. Existing recommendation methods based on semantic similarity, social graphs, or collaborative filtering are unsuitable for very large social networks because of their high computational cost or low effectiveness. We present an approach entitled Hybrid Recommendation Through Community Detection (HRTCD) for friend prediction with linear runtime complexity that makes full use of the characteristics of social media based on hybrid information fusion. It extracts the content topics of microblog for each participant along with the appraisal of domain-dependent user impact, builds a small-size heterogeneous network for each target user by fusing the interest similarity and social interaction between individuals, discovers all of the implicit clusters of target user via a community detection algorithm, and establishes the recommendation set consisting of a fixed number of potential friends. Experimental results on both the synthetic and real-world social networks demonstrate that our scheme provides a higher prediction rating and significantly improves the recommendation accuracy and offers much faster performance.