Heterogeneous Graph Fusion with Adversarial Learning for Recommendation Service

Abstract

Social recommendation, in particular, relies on modeling social information to provide high-order information beyond user-item interaction in recommendation service. However, current approaches rely on graph neural network-based embedding, which can result in an over-smoothing problem. Additionally, graph diffusion, which encodes high-order features, can add noise to the model. Previous research has not adequately addressed the latent influence of social relations. In this work, we introduce a new recommendation framework named Heterogeneous Information Network Fusion with Adversarial Learning (HIN-FusionGAN), which inherently fuses adversarial learning-enhanced social networks with the fused graph between the user-item interaction graph and user-user social graph. We propose a heterogeneous information network that fuses social and interaction graphs into a unified heterogeneous graph, explicitly encoding high-order collaborative signals. We employ user embeddings using both interaction information and adversarial learning-enhanced social networks, which are efficiently fused by the feature fusion model. To address the issue of over-smoothing and uncover latent feature representation, we use the structure of an adversarial network in social relation graph. Comprehensive experiments on three real-world datasets demonstrate the superiority of our proposed model.