Human mobility prediction forecasts a user's next Point of Interest (POI) from historical trajectories, supporting applications from recommendation to urban planning. Recent studies have recognized the problem with long-tail POIs in human mobility prediction, which are POIs with few visit records, making new visits to such POIs difficult to predict. Our analysis shows that many predictions fail even for visits to popular POIs. The underlying cause is often transition-level sparsity: the corresponding source-destination transition appears rarely, or never appears, in the training set. We therefore argue that a core bottleneck in human mobility prediction lies in transition-level long-tail generalization. We formulate this problem as compositional generalization and propose a tRansition rEconstruction framework for Compositional generAlization in next-POI prediction (RECAP). RECAP reconstructs long-tail transitions from two generalizable signals: multi-hop transitivity in the global transition graph and revisit evidence from a user's historical trajectory. It further uses warm-transition holdout training to discourage memorization of frequent transitions and encourage generalization from transferable signals. Experiments on multiple real-world datasets show that RECAP consistently improves prediction accuracy, with clear gains on tail transitions.

翻译：人类移动性预测旨在从历史轨迹中推测用户的下一个兴趣点，支持从推荐到城市规划等应用。近年研究已认识到长尾兴趣点（即访问记录稀少的兴趣点）带来的预测难题——对新访问此类兴趣点的预测尤为困难。我们的分析表明，即便是对热门兴趣点的访问预测也常失败，其根本原因常在于转移层面的稀疏性：训练集中相应的源-目标转移出现频率极低甚至从未出现。因此我们认为，人类移动性预测的核心瓶颈在于转移层面的长尾泛化。我们将此问题形式化为组合泛化，并提出面向下一兴趣点预测的组合泛化转移重建框架（RECAP）。RECAP从两个可泛化信号中重建长尾转移：全局转移图中的多跳传递性，以及用户历史轨迹中的重访证据。此外，它采用温转移留出训练策略，抑制对频繁转移的记忆，鼓励从可迁移信号中进行泛化。在多个真实数据集上的实验表明，RECAP能持续提升预测准确率，尤其在尾部转移上取得显著改进。