CLLP: Contrastive Learning Framework Based on Latent Preferences for Next POI Recommendation

Next Point-Of-Interest (POI) recommendation plays an important role in various location-based services.Its main objective is to predict the users' next interested POI based on their previous check-in information.Most existing studies view the next POI recommendation as a sequence prediction problem but pay little attention to the fine-grained latent preferences of users, neglecting the diversity of user motivations on visiting the POIs.In this paper, we propose a contrastive learning framework based on latent preferences (CLLP) for next POI recommendation, which models the latent preference distributions of users at each POI and then yield disentangled latent preference representations.Specifically, we leverage the cross-local and global spatio-temporal contexts to learn POI representations for dynamically modeling user preferences.And we design a novel distillation strategy to make full use of the collaborative signals from other users for representation optimization.Then, we disentangle multiple latent preferences in POI representations using predefined preference prototypes, while leveraging preference-level contrastive learning to encourage independence of different latent preferences by improving the quality of latent preference representation space.Meanwhile, we employ a multi-task training strategy to jointly optimize all parameters.Experimental results on two real-world datasets show that CLLP achieves the state-of-the-art performance and significantly outperforms all existing solutions.Further investigations demonstrate the robustness of CLLP against sparse and noisy data.