TV SCHEDULING FRAMEWORK USING AI FOR ENHANCED VIEWER ENGAGEMENT

The rapid evolution of television broadcasting and the growing demand for personalized content delivery have highlighted the limitations of traditional linear TV schedules. These static systems often fail to adapt to real-time audience preferences, hindering engagement and limiting advertising potential. This paper proposes an AI-driven real-time scheduling framework that integrates deep learning with GA-optimized Q-learning to dynamically optimize TV programming. By leveraging historical viewership data, real-time social media trends, external influencing factors, and audience analytics, the model forecasts engagement levels for different time slots. This adaptive scheduling system allows for real-time adjustments to the broadcasting schedule, enhancing viewer engagement, maximizing advertisement revenue, and improving overall broadcasting efficiency.