解决用户移动预测中冷门地点难预测问题，提升稀有路线的泛化能力。
Beyond Long Tail POIs: Transition-Centered Generalization for Human Mobility Prediction
- 基于全局转移图的多跳传递性和用户历史重访证据重建稀有转移路径
- 在多个真实数据集上显著提升尾部转移预测准确率，尤其对低频地点有效
- 适合做移动推荐、城市规划等需要精准预测稀有行为的应用场景
人类移动预测旨在根据历史轨迹预测用户下一个兴趣点（POI），支持推荐与城市规划等应用。现有研究关注长尾POI（访问记录少的地点）带来的挑战，但我们的分析发现，即使热门地点也常预测失败。根本原因在于转移层面的稀疏性：目标源-目的地转移在训练集中出现频率极低或从未出现。因此我们提出过渡中心的泛化瓶颈是核心障碍。将此问题建模为组合泛化，并提出RECAP框架，通过全局转移图的多跳传递性与用户历史重访证据重构长尾转移。同时采用暖过渡保留训练策略，抑制对高频转移的记忆，促进可迁移信号的泛化。在多个真实数据集上的实验表明，RECAP持续提升预测精度，对尾部转移有明显增益。
原文摘要 · Abstract (English)
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.
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