  In Location-Based Services, Point-Of-Interest(POI) recommendation plays a
crucial role in both user experience and business opportunities. Graph neural
networks have been proven effective in providing personalized POI
recommendation services. However, there are still two critical challenges.
First, existing graph models attempt to capture users' diversified interests
through a unified graph, which limits their ability to express interests in
various spatial-temporal contexts. Second, the efficiency limitations of graph
construction and graph sampling in large-scale systems make it difficult to
adapt quickly to new real-time interests. To tackle the above challenges, we
propose a novel Spatial-Temporal Graph Interaction Network. Specifically, we
construct subgraphs of spatial, temporal, spatial-temporal, and global views
respectively to precisely characterize the user's interests in various
contexts. In addition, we design an industry-friendly framework to track the
user's latest interests. Extensive experiments on the real-world dataset show
that our method outperforms state-of-the-art models. This work has been
successfully deployed in a large e-commerce platform, delivering a 1.1% CTR and
6.3% RPM improvement.
