Method for integrating multimodal generative models into DSP platforms with semantic control for dynamic advertising content generation

DOI: 10.31673/2412-9070.2025.061210

Abstract

The article presents a scientific method for integrating multimodal generative models into Demand-Side Platforms (DSPs) to enable dynamic generation of advertising content based on user be havioral features. The proposed architecture consists of four interconnected modules: a prompt generator, a multimodal content generator, a semantic control module, and a DSP connector.
A key contribution of this research is the development of the semantic control module, which performs multi-level verification of generated content, including linguistic safety, visual consistency, and brand compliance. A mathematical optimization model is proposed to maximize content utility by balancing semantic similarity, contextual relevance, and content safety.
Experimental modeling was carried out using simulated data of 10,000 user sessions. The results demonstrated significant improvements compared to a baseline DSP campaign: click-through rate (CTR) increased by 24 %, engagement rate (ER) by 18 %, and conversion rate (CR) by 12 %. Statistical testing confirmed the reliability of these improvements (p < 0.05).
The developed method provides a scalable framework for adaptive, AI-driven content generation within DSP ecosystems. Its practical implementation through REST APIs allows advertisers to automate creative production, enhance personalization, and ensure semantic and ethical control of advertising content.

Keywords: multimodal models; DSP; generative artificial intelligence; semantic control; adaptive advertising; content security.