HyperMST: Multi-scale Spatio-Temporal Hypercorrelation Network for POI Recommendation

Abstract

Point-of-Interest (POI) recommendation has become increasingly important in the trajectory prediction domain. However, most existing approaches focus on a single scale and tend to overemphasize either spatial or temporal aspects. These methods often overlook the temporal dependencies in movement behavior and fail to fully exploit the interplay between spatial and temporal correlations, which limits their overall effectiveness. In this paper, we propose a Multi-scale Spatio-Temporal Hypercorrelation Network (HyperMST) for POI recommendation. HyperMST leverages geographic and historical information through cross-shared Graph Convolutional Networks (GCNs), while employing a multi-scale hypergraph attention mechanism to capture dynamic POI correlations across varying temporal scales. Furthermore, the model enhances prediction accuracy by incorporating memory-enhanced embeddings and a spatio-temporal coherence mechanism. Extensive experiments on two real-world public datasets demonstrate that our model significantly outperforms state-of-the-art methods, establishing a new benchmark in POI recommendation.