DCal-Rec: A Spatio-Temporal Distribution Calibration Framework for Next-POI Recommendation

The rapid expansion of mobile user behavior data has made next-Point-of-Interest (POI) recommendation increasingly vital for enhancing personalized location-based services. However, the non-uniform spatio-temporal distribution of user behavior poses significant challenges to recommendation performance. Most existing methods neglect this fundamental issue at the distribution level, while conventional data augmentation strategies fall short in optimizing spatio-temporal distribution properties. To tackle this problem, we propose a spatio-temporal Distribution Calibration framework for next-POI Recommendation (DCal-Rec), which optimizes behavioral sequence distributions through disentangled spatial and temporal operator pools. This is combined with a dual-constraint mechanism that incorporates both distribution and interest information to maintain semantic consistency. Furthermore, a multi-channel contrastive learning paradigm is introduced to jointly optimize the recommendation and contrastive tasks under a unified training objective, thereby improving the model’s generalization capability. Experimental results on three public real-world datasets demonstrate that DCal-Rec significantly outperforms baseline methods across various evaluation metrics.