STGCN: A Spatial-Temporal Aware Graph Learning Method for POI Recommendation

Abstract

Point-of-Interest (POI) recommendation helps users find their interested places to visit based on the time and user location. Unlike traditional recommendation tasks, POI recommendation is personalized, spatial-aware, and temporally dependent. Although many previous works have tried modeling spatial and temporal characteristics, most of them suffer from the following two limitations: For the spatial aspect, existing works only consider the user-POI distance or POI-POI distance. However, we find that a user prefers different regions at different times, which is known as user-region periodic pattern. For the temporal aspect, most works treat user and time as two independent factors. However, different users may prefer the same POI in different time periods, which is known as user-POI periodic pattern. To address the limitation of existing works, we propose a novel Spatial-Temporal aware Graph Convolutional Neural Network (STGCN) for POI recommendation. Specifically, we first design a user record multigraph to fuse all the context information into a unified graph. Then, we propose a time-based neighborhood sampling algorithm and take advantage of the flexible propagation mechanism of GCNs to learn the representations of each node at a specific time. Furthermore, multiple scoring functions are proposed to exploit user-region periodic pattern and user-POI periodic pattern, respectively. We also develop a time smoothing strategy to alleviate the data sparsity problem. Extensive experiments are conducted on two real-world datasets, and the experimental results demonstrate the effectiveness of our method.