The paper introduces U-MASK, a novel user-adaptive spatio-temporal masking method that resolves the tension between immediacy, stability, and generalization in personalized mobile AI applications. It unifies short-term adaptation, long-horizon forecasting, and cold-start recommendations by formulating them as a conditional completion problem on partially observed spatio-temporal data, demonstrating significant improvements on real-world mobile datasets.