In the rapidly evolving domain of large-scale retail data systems, envisioning and simulating future consumer transactions has become a crucial area of interest. It offers significant potential to fortify demand forecasting and fine-tune inventory management. This paper presents an innovative application of Generative Adversarial Networks (GANs) to generate synthetic retail transaction data, specifically focusing on a novel system architecture that combines consumer behavior modeling with stock-keeping unit (SKU) availability constraints to address real-world assortment optimization challenges. We diverge from conventional methodologies by integrating SKU data into our GAN architecture and using more sophisticated embedding methods (e.g., hyper-graphs). This design choice enables our system to generate not only simulated consumer purchase behaviors but also reflects the dynamic interplay between consumer behavior and SKU availability -- an aspect often overlooked, among others, because of data scarcity in legacy retail simulation models. Our GAN model generates transactions under stock constraints, pioneering a resourceful experimental system with practical implications for real-world retail operation and strategy. Preliminary results demonstrate enhanced realism in simulated transactions measured by comparing generated items with real ones using methods employed earlier in related studies. This underscores the potential for more accurate predictive modeling.

翻译：在快速演进的大规模零售数据系统领域，对未来消费者交易进行设想与模拟已成为关键研究方向。该领域在强化需求预测与优化库存管理方面展现出巨大潜力。本文提出一种生成对抗网络（GANs）的创新应用，用于生成合成零售交易数据，特别聚焦于一种新颖的系统架构——该架构将消费者行为建模与库存单位（SKU）可用性约束相结合，以应对现实世界中的商品分类优化挑战。我们通过将SKU数据整合至GAN架构并采用更先进的嵌入方法（如超图），突破了传统方法的局限。这种设计使系统不仅能生成模拟的消费者购买行为，还能反映消费者行为与SKU可用性之间的动态交互作用——这一维度在传统零售仿真模型中常因数据稀缺而被忽视。我们的GAN模型能在库存约束下生成交易数据，开创性地构建了一个具有现实零售运营与战略意义的实验系统。初步结果表明，通过采用相关研究中既有的方法比较生成商品与真实商品，模拟交易的真实性得到显著提升，这为构建更精准的预测模型奠定了坚实基础。