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arXiv · 2303.12507

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

Yan Luo·Ye Liu·Fu-lai Chung·Yu Liu·Chang Wen Chen
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Abstract

Predicting the next location is a highly valuable and common need in many location-based services such as destination prediction and route planning. The goal of next location recommendation is to predict the next point-of-interest a user might go to based on the user's historical trajectory. Most existing models learn mobility patterns merely from users' historical check-in sequences while overlooking the significance of user preference modeling. In this work, a novel Point-of-Interest Transformer (POIFormer) with contrastive user preference 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 patterns from historical check-in sequences, while query generator explicitly learns user preferences to generate user-specific intention queries. Finally, preference decoder combines the intention queries and historical information to predict the user's next location. Extensive comparisons with representative schemes and ablation studies on four real-world datasets demonstrate the effectiveness and superiority of the proposed scheme under various settings.

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Yan Luo, Ye Liu, Fu-lai Chung, Yu Liu, Chang Wen Chen. 2023-03-22. End-to-End Personalized Next Location Recommendation via Contrastive User Preference Modeling. https://arxiv.org/abs/2303.12507

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