Microblog User Recommendation Based on Particle Swarm Optimization

Considering that there exists a strong similarity between behaviors of users and intelligence of swarm of agents, in this paper we propose a novel user recommendation strategy based on particle swarm optimization (PSO) for Microblog network. Specifically, a PSO-based algorithm is developed to learn the user influence, where not only the number of followers is incorporated, but also the interactions among users (e.g., forwarding and commenting on other users' tweets). Three social factors, the influence and the activity of the target user, together with the coherence between users, are fused to improve the performance of proposed recommendation strategy. Experimental results show that, compared to the well-known PageRank-based algorithm, the proposed strategy performs much better in terms of precision and recall and it can effectively avoid a biased result caused by celebrity effect and zombie fans effect.