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Predicting the next location is a highly valuable and common need in manylocation-based services such as destination prediction and route planning. Thegoal of next location recommendation is to predict the next point-of-interest auser might go to based on the user's historical trajectory. Most existingmodels learn mobility patterns merely from users' historical check-in sequenceswhile overlooking the significance of user preference modeling. In this work, anovel Point-of-Interest Transformer (POIFormer) with contrastive userpreference modeling is developed for end-to-end next location recommendation.This model consists of three major modules: history encoder, query generator,and preference decoder. History encoder is designed to model mobility patternsfrom historical check-in sequences, while query generator explicitly learnsuser preferences to generate user-specific intention queries. Finally,preference decoder combines the intention queries and historical information topredict the user's next location. Extensive comparisons with representativeschemes and ablation studies on four real-world datasets demonstrate theeffectiveness and superiority of the proposed scheme under various settings.

End-to-End Personalized Next Location Recommendation via Contrastive User Preference Modeling