A novel social network hybrid recommender system based on hypergraph topologic structure

With the advent and popularity of social network, more and more people like to share their experience in social network. However, network information is growing exponentially which leads to information overload. Recommender system is an effective way to solve this problem. The current research on recommender systems is mainly focused on research models and algorithms in social networks, and the social networks structure of recommender systems has not been analyzed thoroughly and the so-called cold start problem has not been resolved effectively. We in this paper propose a novel hybrid recommender system called Hybrid Matrix Factorization(HMF) model which uses hypergraph topology to describe and analyze the interior relation of social network in the system. More factors including contextual information, user feature, item feature and similarity of users ratings are all taken into account based on matrix factorization method. Extensive experimental evaluation on publicly available datasets demonstrate that the proposed hybrid recommender system outperforms the existing recommender systems in tackling cold start problem and dealing with sparse rating datasets. Our system also enjoys improved recommendation accuracy compared with several major existing recommendation approaches.