Next Point of Interest Recommendation Based on Graph Structure and Sequential Pattern

In location-based social networks, next point of interest (POI) recommendation predicts the next-visited POIs of users by mining their behavioral patterns. However, existing POI recommendation methods based on graph neural networks and attention mechanisms fail to adequately capture: 1) the local structural features influenced by the trajectories of other users (i.e., the relationships between POIs visited by users); and 2) the dynamic visitation preferences and channel relationships among POIs (i.e., interdependencies between contextual features). We propose a next-POI recommendation model based on graph structure and sequential pattern to address these limitations. The model generates graph representations that reflect the real-time preferences of users by extracting local structures from a global POI graph. In addition, we design a temporal-aware self-attentive graph convolutional network and a channel attention mechanism to capture the structural features of users’ sequential visitation tendencies and the latent feature-channel relationships between POIs, respectively. These components enhance the ability of the model to characterize dynamic user preferences and behavioral changes. The results demonstrate that our model outperforms baseline methods on three real-world datasets, validating its effectiveness in capturing both global and local information.