A Multi-Feature Transition-Aware Framework for Next POI Recommendation

Next Point-of-Interest (POI) recommendation aims to predict a user’s next location based on historical check-in data. However, real-world check-in records often contain uncertain check-ins, in which ambiguous spatial, temporal, or behavioral information obscures true mobility patterns and degrades prediction accuracy. To mitigate this issue, this study first learns user preferences from historical trajectories and adjusts transition importance based on temporal and spatial proximity, before modeling transition relationships using three complementary features: category, spatial area, and routine/non-routine behavior patterns. Based on transition probability analysis, feature-level dependencies in user mobility are systematically examined. The results indicate that these transition features contribute unequally to prediction performance, with area-based transitions being the most effective when considered individually. Nevertheless, their integration consistently yields the highest accuracy, highlighting the importance of transition-aware modeling. Experiments on two real-world datasets demonstrate that the proposed framework outperforms state-of-the-art methods in terms of Recall and NDCG, confirming the effectiveness of the proposed approach.