Generative modelling of multi-user datasets has become prominent in science and engineering. Generating a data point for a given user requires employing user information, and conventional generative models, including variational autoencoders (VAEs), often ignore this. This paper introduces GUIDE-VAE, a novel conditional generative model that leverages user embeddings to generate user-guided data. By leveraging shared patterns across users, GUIDE-VAE improves performance in multi-user settings, even under significant data imbalance. In addition to integrating user information, GUIDE-VAE incorporates a pattern dictionary-based covariance composition (PDCC) to improve the realism of generated samples by capturing complex feature dependencies. While user embeddings drive performance gains, PDCC addresses common issues such as noise and over-smoothing typically seen in VAEs. The proposed GUIDE-VAE was evaluated on a multi-user smart meter dataset characterised by substantial data imbalance across users. Quantitative results show that GUIDE-VAE performs effectively on both synthetic data generation and missing-record imputation tasks, while qualitative evaluations indicate that it produces more plausible and less noisy data. These results establish GUIDE-VAE as a promising tool for controlled, realistic data generation in multi-user datasets, with potential applications across domains that require user-informed modelling.

翻译：多用户数据集的生成建模已成为科学和工程领域的重要课题。为特定用户生成数据点需要利用用户信息，而传统生成模型（包括变分自编码器VAEs）常常忽略这一点。本文提出GUIDE-VAE，一种新颖的条件生成模型，通过利用用户嵌入来生成用户引导的数据。通过挖掘用户间的共享模式，GUIDE-VAE即使在数据严重不平衡的情况下也能提升多用户场景的性能。除整合用户信息外，GUIDE-VAE还引入基于模式字典的协方差组合（PDCC），通过捕捉复杂的特征依赖关系来增强生成样本的真实性。用户嵌入驱动性能提升，而PDCC则解决了VAE中常见的噪声和过度平滑等问题。所提出的GUIDE-VAE在用户间数据高度不平衡的多用户智能电表数据集上进行了评估。定量结果表明，GUIDE-VAE在合成数据生成和缺失记录插补任务中均表现有效，而定性评估显示其能生成更合理且噪声更少的数据。这些结果确立GUIDE-VAE作为多用户数据集中可控、真实数据生成的有力工具，在需要用户信息建模的领域具有潜在应用价值。