  Social recommendation, which utilizes social relations to enhance recommender
systems, has been gaining increasing attention recently with the rapid
development of online social network. Existing social recommendation methods
are based on the fact that users preference or decision is influenced by their
social friends' behaviors. However, they assume that the influences of social
relation are always the same, which violates the fact that users are likely to
share preference on diverse products with different friends. In this paper, we
present a novel CSR (short for Characterized Social Regularization) model by
designing a universal regularization term for modeling variable social
influence. Our proposed model can be applied to both explicit and implicit
iteration. Extensive experiments on a real-world dataset demonstrate that CSR
significantly outperforms state-of-the-art social recommendation methods.
