Abstract
Next point-of-interest (POI) recommendation seeks to predict the POI that a user will visit next from historical check-in records. Accurate recommendations require modeling POI relations and user trajectory patterns. Existing methods remain limited in representing continuous spatial influence decay, reliability-filtered temporal associations, semantic hierarchies, and multi-membership attributes. Furthermore, user trajectories may contain relatively persistent behavioral regularities and short-term variations within a check-in sequence, while standard time-domain models do not explicitly decompose trajectory representations by variation scale. Accordingly, this study introduces the Dynamic Hierarchical Attention Fusion Spatio-Temporal Network (DHAFST). Our Adaptive Dual-Graph Convolutional Network (ADGCN) employs Gaussian kernels with dataset-specific bandwidths to model continuous spatial influence and uses activity- and co-occurrence-based reliability filtering to construct temporal associations. The Bidirectional Hierarchical Graph Attention Network (BiHGAN) estimates soft memberships through a nested Gaussian mixture hierarchy and constructs cross-level affiliation relations for bidirectional hierarchical propagation. The Attention-Frequency Fusion Transformer (AFFT) combines time-domain self-attention with FFT-based low-frequency reconstruction and high-frequency residual modeling through an adaptive gate. Experiments on three real-world check-in datasets show that DHAFST achieves the highest Acc@1 among the evaluated methods on all three datasets.