Learning Social Relationship Strength via Matrix Co-Factorization with Multiple Kernels

Youliang Zhong, Lan Du, Jian Yang

Abstract: In recent years the research on measuring relationship strength among the people in a social network has gained attention due to its potential applications of social network analysis. The challenge is how we can learn social relationship strength based on various resources such as user profiles and social interactions. In this paper we propose a KPMCF model to learn social relationship strength based on users’ latent features inferred from both profile and interaction information. The proposed model takes an uniformed approach of integrating Matrix Co-Factorization with Multiple Kernels. We conduct experiments on real-world data sets for typical web mining applications, showing that the proposed model produces better relationship strength measurement in comparison with other social factors.