AI-DRIVEN HYBRID MARKOV MODELS FOR TV CONTENT FORECASTING AND AD PLACEMENT OPTIMIZATION

Abstract

This research introduces a novel AI-driven framework integrating Hybrid Markov Models (HMM), Reinforcement Learning (RL), and Long Short-Term Memory (LSTM) networks for real-time TV content forecasting and dynamic ad placement. Leveraging the sequential decision-making capabilities of HMM with the long-term dependency modeling of LSTM networks enhances content prediction accuracy. Simultaneously, RL optimizes ad placements based on viewer interactions to maximize engagement and revenue efficiently. The proposed model adapts to viewer preferences and trends in real-time, significantly outperforming traditional methods in accuracy and viewer satisfaction. This study contributes to digital media technologies by offering scalable solutions for personalized content delivery and advertisement strategies, illustrating significant advancements in adaptive broadcasting.