This document proposes a user-service rating prediction approach that explores social users' rating behaviors. It focuses on aspects of users' rating behaviors like when they rate items, what the ratings and items are, and how ratings diffuse among social friends. The approach represents rating schedules and models interpersonal rating diffusion. It fuses personal interest factors, interpersonal interest similarity, rating behavior similarity, and rating diffusion into a matrix factorization framework. The proposed approach aims to address limitations of existing collaborative filtering recommender systems like increased computational costs, lack of privacy, and insecure computations.