Applying Graph Convolution Networks to Recommender Systems based on graph topology

Abstract

The recommender systems are widely used in online applications to suggest products to the potential users. The main aim of recommender system is to produce meaningful recommendation to a potential user by monitoring user’s purchasing habits, history, and useful information. Recently, graph representation learning methods based on node embedding have drawn attention in Recommender systems such as Graph Convolutional Networks (GCNs) that is powerful method for collaborative filtering. The GCN performs neighborhood aggregation mechanism to extract high level representation for both user and items. In this paper, we propose a recommendation algorithm based on node similarity convolutional matrices with topological property in GCNs where the linkage measure is illustrated as a bipartite graph. The experiments indicate the necessity of capturing user–item graph structure in recommendation. The experimental results show that node similarity-based convolution matrices and GCN-based embeddings significantly improve the prediction accuracy in recommender systems compared to state-of-art approaches.The recommender systems are widely used in online applications to suggest products to the potential users. The main aim of recommender system is to produce meaningful recommendation to a potential user by monitoring user’s purchasing habits, history, and useful information. Recently, graph representation learning methods based on node embedding have drawn attention in Recommender systems such as Graph Convolutional Networks (GCNs) that is powerful method for collaborative filtering. The GCN performs neighborhood aggregation mechanism to extract high level representation for both user and items. In this paper, we propose a recommendation algorithm based on node similarity convolutional matrices with topological property in GCNs where the linkage measure is illustrated as a bipartite graph. The experiments indicate the necessity of capturing user–item graph structure in recommendation. The experimental results show that node similarity-based convolution matrices and GCN-based embeddings significantly improve the prediction accuracy in recommender systems compared to state-of-art approaches.