Trust prediction and information item rating are two fundamental tasks for social rating network systems.In response to data sparsity of trust relation and rating information encountered when improving the accuracy of predicting the two basic problems

we present a joint model of rating and trust prediction based on collective matrix factorization.In our model

trust relation matrix and information rating matrix are factorized into latent features matrixes collectively.We can make full use of correspondence among users and information items by sharing latent user feature.Moreover

our model can capture the data dependent effect of trust domains and rating domain separately.By using those learned latent features matrixes multiplication

we can obtain predictions of trust and rating.Experimental results on two real network data demonstrate that our model is more accurate than other state-of-the-art methods.