STCD-Hawkes: Spatio-Temporal Causal Debiasing with Hawkes Process for Next POI Recommendation

Next-point-of-interest recommendation is essential for location-based services but remains challenging because of the complex spatio-temporal characteristics of user mobility. Existing methods often treat temporal and spatial information as static features or rely on simple fusion strategies, overlooking both the distributional heterogeneity between temporal triggers and spatial choices and the causal entanglement in which contextual constraints, such as distance, obscure users’ intrinsic preferences. To address these limitations, we propose STCD-Hawkes, a unified probabilistic framework for spatio-temporal causal debiasing with Hawkes processes. The framework decomposes next-POI recommendation into two coupled generative processes. To model when a user moves, it employs a time-aware Hawkes process with a rotation-based adaptation mechanism to capture the continuous evolution of temporal intensity. To predict where the user goes, it introduces a score-based latent diffusion model that generates optimal spatial representations from noise under the condition of the learned temporal intensity. To mitigate confounding effects, it further incorporates a causal disentanglement module based on a disentangled causal mixture-of-experts architecture with orthogonal regularization, explicitly separating invariant user interests from dynamic contextual sensitivities. Extensive experiments on three real-world datasets demonstrate that STCD-Hawkes significantly outperforms state-of-the-art baselines while providing interpretable insights into the causal factors underlying user mobility.