Synthetic Data Generation Frameworks for Training Retail AI Models at Scale
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V4I1P114Keywords:
Synthetic Data, Retail AI, Generative Adversarial Networks, Scalability, Privacy Compliance, Consumer Behavior ModelingAbstract
The rapid expansion of Artificial Intelligence (AI) deployment in the retail sector necessitates robust, compliant, and scalable data infrastructure. Traditional reliance on raw, sensitive customer data poses significant legal, security, and operational challenges, severely impeding the training of large-scale predictive models. This paper provides an expert-level examination of modern synthetic data generation (SDG) frameworks designed to overcome these limitations. The analysis first categorizes SDG methodologies, emphasizing deep learning approaches such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Subsequently, the paper details advanced, domain-specific retail frameworks, including simulation platforms like RetailSynth, which fuse econometric discrete choice models with generative techniques to model realistic consumer behavior and operational constraints. For instance, literature reports that specialized GANs can generate realistic transactions by incorporating weighted stock constraints, a critical operational parameter often overlooked in general modeling. Finally, the paper articulates a comprehensive tripartite evaluation frameworkassessing Fidelity, Utility, and Privacywhich is essential for validating the analytical equivalence and trustworthiness of synthetic retail datasets. Fidelity metrics such as Wasserstein distance and Jensen-Shannon distance quantify statistical similarity, while Utility is assessed through predictive task performance (Accuracy, Lift, and Conviction). The successful implementation of these frameworks is critical for achieving competitive advantage through scaled, privacy-compliant AI applications like dynamic pricing and advanced demand forecasting. This paper reviews methodologies and frameworks reported in the literature, without presenting new experimental results
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