Joint Rating and Trust Prediction Based on Collective Matrix Factorization
ZHANG Wei-yu;WU Bin;GENG Yu-shui;ZHU Jiang;Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia,Beijing University of Posts and Telecommunications;School of Information,Qilu University of Technology;
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. M oreover,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.