ENHANCING TV CONTENT FORECASTING WITH HYBRID MARKOV MODELS AND AI TECHNIQUES

Abstract

The rapid growth of digital media platforms has heightened the need for accurate TV content forecasting and personalized recommendations. Traditional methods often struggle to keep up with the dynamic nature of modern media consumption. This paper introduces a novel Hybrid AI model combining Markov Chains, Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) to enhance TV content forecasting, optimize content recommendations, and improve advertisement placement. The model integrates Markov Models for sequential behavior prediction and utilizes ML algorithms like Random Forest and SVM to refine predictions based on user demographics. Additionally, Reinforcement Learning is employed to dynamically adjust recommendations and ad placements, maximizing viewer engagement and advertising revenue. LSTM-based Recurrent Neural Networks (RNNs) capture non-linear relationships in viewer behavior, improving long-term prediction accuracy. The methodology is evaluated using data from streaming platforms, with metrics such as prediction accuracy, engagement rates, and computational efficiency. Results show that the hybrid model outperforms traditional approaches, offering a scalable and adaptable solution for modern content forecasting challenges. This research provides a comprehensive framework for TV content forecasting and ad placement optimization, contributing to the development of personalized, intelligent, and responsive viewing experiences.