Luo, Y., Liu, Y., Chung, F. L., Liu, Y., & Chen, C. W. (2023). End-to-End Personalized Next Location Recommendation via Contrastive User Preference Modeling. arXiv preprint arXiv:2303.12507.

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 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. In addition, we further investigate the factors that enable contrastive learning to work effectively in the context of human mobility and conclude that both the category and quantity of Points of Interest (POIs) influence the performance of contrastive learning.