Personalized mobile artificial intelligence applications are widely deployed, yet they are expected to infer user behavior from sparse and irregular histories under a continuously evolving spatio-temporal context. This setting induces a fundamental tension among three requirements, i.e., immediacy to adapt to recent behavior, stability to resist transient noise, and generalization to support long-horizon prediction and cold-start users. Most existing approaches satisfy at most two of these requirements, resulting in an inherent impossibility triangle in data-scarce, non-stationary personalization. To address this challenge, we model mobile behavior as a partially observed spatio-temporal tensor and unify short-term adaptation, long-horizon forecasting, and cold-start recommendation as a conditional completion problem, where a user- and task-specific mask specifies which coordinates are treated as evidence. We propose U-MASK, a user-adaptive spatio-temporal masking method that allocates evidence budgets based on user reliability and task sensitivity. To enable mask generation under sparse observations, U-MASK learns a compact, task-agnostic user representation from app and location histories via U-SCOPE, which serves as the sole semantic conditioning signal. A shared diffusion transformer then performs mask-guided generative completion while preserving observed evidence, so personalization and task differentiation are governed entirely by the mask and the user representation. Experiments on real-world mobile datasets demonstrate consistent improvements over state-of-the-art methods across short-term prediction, long-horizon forecasting, and cold-start settings, with the largest gains under severe data sparsity. The code and dataset will be available at https://github.com/NICE-HKU/U-MASK.

翻译：个性化移动人工智能应用已广泛部署，但其需在持续演化的时空背景下，从稀疏且不规律的历史数据中推断用户行为。这一场景引发了三个需求间的根本性矛盾：适应近期行为的即时性、抵抗瞬时噪声的稳定性，以及支持长时程预测与冷启动用户的泛化性。现有方法大多仅能满足其中至多两项需求，导致在数据稀缺、非平稳的个性化场景中存在固有的"不可能三角"。为应对这一挑战，我们将移动行为建模为部分观测的时空张量，并将短期适应、长时程预测与冷启动推荐统一为条件补全问题，其中用户与任务特定的掩码指定了哪些坐标被视为证据。我们提出U-MASK——一种用户自适应时空掩码方法，该方法基于用户可靠性与任务敏感性分配证据预算。为在稀疏观测下生成掩码，U-MASK通过U-SCOPE模块从应用与位置历史中学习紧凑的任务无关用户表征，作为唯一的语义条件信号。随后，共享的扩散Transformer在保持观测证据的同时执行掩码引导的生成式补全，从而使个性化与任务分化完全由掩码和用户表征控制。在真实移动数据集上的实验表明，本方法在短期预测、长时程预测及冷启动场景中均优于现有最优方法，且在数据极度稀疏时提升最为显著。代码与数据集将在https://github.com/NICE-HKU/U-MASK公开。