LAUSR

In order to improve the performance of recommender systems, user social information and item attribute information should be integrated when building the prediction model, which is a hotspot and difficulty in the field of recommender systems. In this paper, we propose an extended matrix factorization model based on network representation learning. To characterize users and items comprehensively, we construct the user relation network and the item relation network from the multi-source data. Then the representation vectors of users and items are learned from two networks respectively. The representation vectors learned from the relation networks can characterize users and items more effectively. Since users and items belong to different vector spaces, a matrix is used to connect user and item representation vectors when predicting ratings. To obtain the connection matrix, stochastic gradient descent is applied to minimize the errors between the predicted and observed ratings. Experimental results on two real-world datasets, Yelp and Douban, demonstrate the effectiveness of our model compared to the state-of-the-art recommendation algorithms.