AI 中文总结
针对移动使用数据存在的稀疏性、变量异构及使用不平衡问题，提出多变量成像扩散框架MIDiff，通过C-GASF转换数据，在U-Net中用三重注意力保持一致性和依赖性，实验表明该方法在生成移动使用轨迹上性能优异。
AI 中文摘要
移动使用轨迹对用户行为预测和应用推荐等任务至关重要，但受隐私限制和大规模数据收集成本的制约。生成模型在一般时间序列上表现良好，但应用于移动使用数据仍具挑战，因其存在稀疏性、变量类型异构性及使用不平衡等问题。为此提出多变量成像扩散（MIDiff）框架，它在由交叉格拉姆角和场（C-GASF）定义的成像空间中运行。C-GASF将稀疏多变量序列转换为相关图像，MIDiff在U-Net中采用三重注意力来保持时间一致性和变量依赖性。实验表明MIDiff达到了保真度指标的最优性能，特别是其判别准确率为0.1526，而最强基线ZITS-VAE为0.3476，证明了它在生成真实多样的移动使用轨迹方面的有效性。
英文摘要
Mobile usage traces are critical for tasks such as user behavior prediction and app recommendation, yet their use is constrained by privacy restrictions and costly large-scale data collection. Although generative models perform well on general time series, their application to mobile usage data remains challenging because (i) limited user activity causes severe sparsity, (ii) heterogeneous variable types complicate joint modeling, and (iii) functional differences across apps create pronounced usage imbalance. To address these challenges, we propose Multivariate-Imaging Diffusion (MIDiff), a diffusion-based framework operating in an imaging space defined by Cross-Gramian Angular Sum Field (C-GASF). C-GASF transforms sparse multivariate sequences into correlation images, while MIDiff employs Triple Attention in a U-Net to preserve temporal consistency and variable dependencies. Experiments show that MIDiff achieves state-of-the-art performance across fidelity metrics. In particular, it obtains a Discriminative Accuracy (DA) of 0.1526, compared with 0.3476 for the strongest baseline, ZITS-VAE, demonstrating its effectiveness in generating realistic and diverse mobile usage traces. Our code is available at https://github.com/YilaiLiu-HKU/MIDiff.
