IDDiff:Intent-aware Deterministic Diffusion Model for Next Location Prediction

Next location prediction aims to infer users’ next destinations from their histori- cal check-in trajectories. Although diffusion models offer a promising direction due to powerful generative capabilities, existing diffusion-based approaches en- counter two critical challenges: (i) check-in trajectories naturally entangle visits driven by diverse intents. Existing methods rely on raw check-in trajectory repre- sentations as contextual conditions to guide diffusion models, which can lead to learned representations that are inconsistent with the user’s authentic intents, ulti- mately yielding suboptimal performance; (ii) our experiment reveals a limitation when applying standard Gaussian noise to POI recommendation. The injected randomness may disrupt personalized preference structures, limiting their abil- ity to model users’ behaviors. To address these challenges, we propose IDDiff, an Intent-aware Deterministic Diffusion framework for next location prediction. Specifically, the Sequence-aware Trajectory Representation Module constructs a user’s global trajectory-level intent representation. It then divides the trajectory into multiple local subtrajectories and clusters semantically similar sub-trajectory representations to construct trajectory intent prototypes, which serve as contextual conditions to guide the diffusion model. Furthermore, we develop a Intent Refine- ment Module to address the structural disruption and reconstruction uncertainty caused by stochastic noise. Rather than injecting random Gaussian perturbations, the module constructs a predefined and controllable preference transformation tra- jectory, thereby preserving personalized mobility preference structures. Extensive experiments on five real-world datasets demonstrate that IDDiff consistently out- performs state-of-the-art methods. The implementation code is publicly available at https://anonymous.4open.science/r/IDDiff-7B4F.