Point-of-Interest Recommendation and Mobility Routines
Next point-of-interest (POI) recommendation supports personalized place discovery on location-based Web platforms.
Mobility routines can recur across users while being realized at different places and adapted to individual preferences.
DualClusterNet Model Architecture
We present DualClusterNet, a causal sequential recommender that composes two population-shared prototype dictionaries. At each observed prefix, soft routers retrieve pattern and spatial mixtures; a factorized multiplicative interaction couples them, feature-wise affine modulation adapts the shared representation, and a separate user-conditioned branch with an additive--projective gate retains individual variation. The prediction head combines this representation with a learned first-order transition term and ranks the complete training POI vocabulary.
Evaluation and Results
We evaluate on NYC and TKY using a training-only vocabulary and an original-final-target protocol that neither bridges unknown POIs nor substitutes earlier targets.
The available single-seed results show competitive full-candidate ranking, while controlled analyses support the contribution of the compositional and personalized branches.
Code is available at https://anonymous.4open.science/r/DualClusterNet-F4EE/.