Personalized Short-Term and Periodic Long-Term Preferences Modeling With Contrastive Learning for Next POI Recommendation

Personalized Short-Term and Periodic Long-Term Preferences Modeling With Contrastive Learning for Next POI Recommendation

Next point-of-interest (POI) recommendation plays a crucial role in enhancing user travel experiences and driving platform revenues by suggesting potentially appealing locations to users. Recent advancements have focused on capturing the general preferences and dynamic interests of users by modeling long- and short-term trajectories. However, existing longterm models struggle to accurately capture periodic user behaviors beyond simple distinctions such as weekdays/weekends or seasons. Meanwhile, short-term models often follow the assumption that users prefer to visit nearby locations, thereby overlooking the personalized spatial preferences. Furthermore, the interaction between the long- and short-term preferences remains underexplored. To address these gaps, we propose a novel model: personalized short-term and periodic long-term preferences modeling with contrastive learning for next POI recommendation. This model leverages the inherent similarities between a user’s periodic long-term and distance-based shortterm preferences while distinguishing the travel preferences of different users, ultimately improving the accuracy of next POI predictions. Specifically, we introduce a spatial span graph (S2graph) to model the personalized distance span preferences. Additionally, we employ Mamba-based and discrete wavelet transform-based methods to capture long-term periodic patterns. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our proposed model.

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