根据候选地点动态调整用户历史行为的解读，提升下个打卡地预测精度。
CaST-POI: Candidate-Conditioned Spatiotemporal Modeling for Next POI Recommendation
- 用候选地点作查询，动态关注用户历史轨迹，实现条件化建模。
- 在三个数据集上超越现有方法，候选池越大优势越明显。
- 适合需要精准推荐、高并发候选场景的位置服务应用。
下一个兴趣点（POI）推荐在基于位置的服务中至关重要，旨在预测用户的未来移动模式。现有方法通常从历史轨迹中生成单一用户表征，并对所有候选POI进行统一打分。然而，这种不考虑候选的范式忽略了历史访问的相关性实际上依赖于当前评估的候选点。本文提出CaST-POI，一种候选条件化的时空模型用于下一POI推荐。核心思想是：相同的用户历史在评估不同候选POI时应被不同解读。CaST-POI采用候选条件化序列读取器，以候选点作为查询，动态关注用户历史。此外，引入候选相对的时间与空间偏置，捕捉基于历史访问与每个候选点关系的细粒度移动模式。在三个基准数据集上的大量实验表明，CaST-POI持续优于现有最优方法，在多个评估指标上取得显著提升，尤其在大规模候选池下表现突出。
原文摘要 · Abstract (English)
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.
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