AI IN REAL-TIME AD CUSTOMIZATION FOR STREAMING PLATFORMS

Abstract

The rapid growth of streaming platforms has transformed the landscape of television and video consumption. Traditional television advertising, which relied on fixed schedules and generalized targeting, is increasingly being replaced by personalized and context-aware advertising strategies. This paper presents an AI-driven framework for real-time ad customization in streaming platforms, utilizing Generative Adversarial Networks (GANs), Reinforcement Learning (RL), and Long Short-Term Memory (LSTM) networks. These technologies enable dynamic ad content generation, optimal ad placement, and user behavior prediction, ensuring that advertisements are tailored to individual viewer preferences and interactions in real-time. Our experimental results show significant improvements in viewer engagement, prediction accuracy, and revenue generation, demonstrating the effectiveness of AI in transforming the ad experience. This personalized approach not only enhances user satisfaction but also maximizes ad revenue for content providers. The proposed framework provides a comprehensive solution to the challenges of delivering non-intrusive, highly engaging advertisements in a fast-evolving streaming environment.