Interest-Aware Message-Passing Layer-Refined Graph Convolutional Network for Recommendation

Abstract

Graph convolutional networks (GCNs) show great potential in recommendation applications, as they have excellent performance in propagation node information propagation and capturing high-order connectivity in user-item interaction graphs. However, in the current recommendation model based on GCN, incoming information from neighbors is aggregated during information propagation, and some of this information may be noisy due to negative information. Additionally, the over-smoothing problem occurs when the model layers are stacked too high. During the embedding learning of users in the graph convolution operation, an important factor is that high-order neighbor users with no interest are involved, leading to similar embedding for users with no interest. These issues can degrade the recommendation performance. To address these problems, this paper proposes a method called IMPLayerGCN. In this method, high-order graph convolution is performed within subgraphs, which are composed of users with similar interests and their interaction items. The higher-order graph convolution is carried out in the subgraph and the layer representation in the process of GCN re-information propagation and node update is refined. The convolution process uses a symmetrical matrix. This approach avoids the spread of negative information from higher-order neighbors to embedded learning.