A graph neural network framework based on preference-aware graph diffusion for recommendation - Shu, Tao; Shi, Lei; Zhu, Chuangying; Liu, Xia (Frontiers in Psychiatry, 2022)
Transforming user check-in data into graph-structure data is a popular and powerful way to analyze the users’ behaviors in the field of recommendation. Graph-based deep learning methods such as graph embeddings and graph neural net-works have shown promising performance on the task of point-of-interest recommendation in recent years. Despite effectiveness, existing methods fail to capture deep graph structural information, leading the suboptimal representations. In addition, they lack the ability of learning the influences of both global preference and user preference on the check-in behavior. To address the above issues, we propose a general framework based on preference-aware graph diffusion, named PGD. We first construct two type of graphs to represent the global preference and user preference. Then we apply a graph diffusion process to capture the structural information of the generated graphs, resulting in weighted adjacency matrices. Finally, graph neural network-based backbones are introduced to learning the representations of users and POIs on weighted adjacency matrices. A learnable aggregation module is developed to learn the final representations from global preference and user preference adaptively. Extensive experiments on four real-world datasets demonstrate the superiority of PGD on POI recommendation, compared to the mainstream graph -based deep learning methods.