User Recommendation Based on Network Structure in Social Networks

Advances in Web 2.0 technology has led to the popularity of social networking sites. One fundamental task for social networking sites is to recommend appropriate new friends for users. In recent years, network structure has been used for user recommendation. Most existing network structure-based recommendation methods either need to pre-specify the group number and structure type or fail to improve performance. In this paper, we propose a novel network structure-based user recommendation method, called Bayesian nonparametric mixture matrix factorization (BNPM-MF). The BNPM-MF model first employs a Bayesian nonparametric model to automatically determine the group number and the network structure in networks and then applies a matrix factorization method on each structure to user recommendation for improvement. Experiments conducted on a number of real networks demonstrate that the BNPM-MF model is competitive with other state-of-the-art methods.