The Optimization of Advertising Content and Prediction of Consumer Response Rate Based on Generative Adversarial Networks

In digital advertising, accurately capturing consumer preferences and generating engaging, personalized content are essential for effective ad optimization. However, traditional methods often rely on single-modal data or static models, limiting their adaptability to dynamic consumer behavior and complex, multi-dimensional preferences. To address these challenges, we propose a Multi-modal Adaptive Generative Adversarial Network for Ad Optimization and Response Prediction (MAGAN-ORP). MAGAN-ORP integrates multi-modal data—including text, image, and behavioral features—into a unified framework, enabling a comprehensive understanding of consumer preferences. The model includes an adaptive feedback mechanism that dynamically refines ad content based on real-time consumer interactions, ensuring relevancy in evolving environments. Additionally, a consumer response prediction module anticipates user engagement, allowing for proactive optimization of ad strategies.