A Tensor Factorization Based User Influence Analysis Method with Clustering and Temporal Constraint

User influence analysis in social media has attracted tremendous interest from both the sociology and social data mining. It is becoming a hot topic recently. However, most approaches ignore the temporal characteristic that hidden behind the comments and articles of users. In this paper, we introduce a Tensor Factorization based on User Cluster (TFUC) model to predict the ranking of users’ influence in micro blogs. Initially, TFUC obtain an influential users cluster by neural network clustering algorithm. Then, TFUC choose influential users to construct tensor model. A time matrix restrain TFUC expect CP decomposition and ranked users by their influence score that obtained from predicted tensor at last. Our experimental results show that the MAP of TFUC is higher than existing influence models with 3.4% at least.