Local Matrix Factorization with Network Embedding for Recommender Systems

In recommender systems, the rating matrix is usually not a global low-rank but local low-rank. Constructing low-rank submatrices for matrix factorization can improve the accuracy of rating prediction. This paper proposes a novel network embedding-based local matrix factorization model, which can built more meaningful sub-matrices. To alleviate the sparsity of the rating matrix, the social data and the rating data are integrated into a heterogeneous information network, which contains multiple types of objects and relations. The network embedding algorithm extracts the node representations of users and items from the heterogeneous information network. According to the correlation of the node representations, the rating matrix is divided into different sub-matrices. Finally, the matrix factorization is performed on the sub-matrices for rating prediction. We test our network embedding-based method on two real-world public data sets (Yelp and Douban). Experimental results show that our method can obtain more accurate prediction ratings.