关键词: 推荐系统, 协同过滤, 二部网络, 网络表示学习, 矩阵分解

Abstract: Matrix Factorization algorithm is widely used in recommendation systems.However,its performance is often affected by data sparsity and high dimensionality of the data,and less consideration is given to the content information of items.In view of the above problems,a Bipartite Network Representation learning regularized Matrix Factorization recommendation algorithm (BiNRMF) was proposed.Two bipartite networks were constructed using the rating information and tag information of the item respectively.Low-dimensional vectors of users and items were obtained by Bipartite Network Embedding (BiNE) algorithm,which could be applied to calculate the similarity between users and between items.Traditional matrix factorization model was regularized by these similarities.Experimental results on GoodBooks and MovieLens datasets demonstrated a significant improvement in prediction accuracy compared to the classical recommendation algorithm.

Key words: recommendation systems, collaborative filtering, bipartite network, network representation learning, matrix factorization