Research on E-commerce Personalized Visual Marketing Algorithms Based on Generative Adversarial Network (GAN) Model

This paper proposes an AIGC-driven personalized visual marketing method based on GAN models to meet the demand for e-commerce visual marketing. It develops an innovative system that includes a GAN architecture, personalized recommendation algorithms, and visual content optimization, validated through experiments on large-scale e-commerce datasets. The results show that the GAN-based model significantly improves image realism, recommendation effectiveness, and marketing conversion rates compared to traditional systems. This approach provides strong technical support and theoretical foundation for personalized visual marketing and offers valuable insights for AIGC-driven marketing innovations.