EAGCN: An Efficient Adaptive Graph Convolutional Network for Item Recommendation in Social Internet of Things

In the era of Internet of Things (IoT), intelligent recommendation is playing an important role in our daily life. How to provide personalized information to users is the core concern of Internet content service providers. To improve the recommendation quality, it is a hot topic to go beyond merely user–item interaction records and take social relations into account in IoT. Recently, emerged graph neural networks (GNNs) shine a light on simulating the recursive social diffusion process, to refine user embedding learning. Nevertheless, two key issues have not been well studied in previous studies: 1) they usually model user preference and social influence within a same semantic space and fail to simultaneously inject high-order connectivity information reflected in both user–item interaction graph and user–user social graph and 2) they typically rely on negative sampling to optimize a recommendation model, which makes them highly sensitive to the design of the sampler and hardly makes full use of GPU's computing ability. In light of this, we propose a novel framework for item recommendation, namely, an efficient adaptive graph convolutional network (EAGCN). Specifically, we introduce a space-adaptive graph convolutional module, which could jointly explore the propagation process of user interest and social influence. Furthermore, a user-specific gating mechanism is designed to aggregate user representations from both spaces. To make EAGCN practical in social IoT, we devise a fast nonsampling leaner to optimize EAGCN's parameters with better leveraging matrix computing of GPU. Extensive experiments under four scenarios show that our solution consistently and significantly outperforms strong baseline methods in both model effectiveness and training efficiency.