CaST-POI: Candidate-Conditioned Spatiotemporal Modeling for Next POI Recommendation

2026-02-10

arXiv.org

AI Summary

本文提出CaST-POI模型，通过结合候选位置的地理坐标和访问历史的时间距离，改进了基于用户轨迹的下一个兴趣点推荐方法，提高了推荐准确性。

Abstract

Next Point-of-Interest (POI) recommendation plays a crucial role in location-based services by predicting users'future mobility patterns. Existing methods typically compute a single user representation from historical trajectories and use it to score all candidate POIs uniformly. However, this candidate-agnostic paradigm overlooks that the relevance of historical visits inherently depends on which candidate is being evaluated. In this paper, we propose CaST-POI, a candidate-conditioned spatiotemporal model for next POI recommendation. Our key insight is that the same user history should be interpreted differently when evaluating different candidate POIs. CaST-POI employs a candidate-conditioned sequence reader that uses candidates as queries to dynamically attend to user history. In addition, we introduce candidate-relative temporal and spatial biases to capture fine-grained mobility patterns based on the relationships between historical visits and each candidate POI. Extensive experiments on three benchmark datasets demonstrate that CaST-POI consistently outperforms state-of-the-art methods, yielding substantial improvements across multiple evaluation metrics, with particularly strong advantages under large candidate pools. Code is available at https://github.com/YuZhenyuLindy/CaST-POI.git.

Problem

• Next POI Recommendation
• Spatiotemporal Modeling
• Geographic Coordinates
• Candidate-Conditioned

Innovation

• candidate-conditioned
• spatiotemporal modeling
• target attention
• geographic distance
• bucketised biases