Predicting television audience numbers precisely serves broadcasters to create better programming schedules and strengthen their advertisement tactics. The conventional forecasting methods fail to detect outlier patterns which prevents them from understanding viewer participation effectively. This research develops a machine learning predictive system which combines audience analytics with recommendations and forecasting to achieve more accurate predictions. Audience segmentation and profiling processes use the methods Non-Negative Matrix Factorization (NNMF) and Field-aware Factorization Machines (FFM). AI clustering algorithms group audience members into segmented categories based on their preferences to enhance recommended content recommendations. The collaborative filtering methods NNMF and FFM group audience segments through anonymized viewer activities which creates implicit preference-based segments. The AI-based recommendation strategy includes two scenarios: content switch displays customized show trailers in place of generic ones depending on viewer preferences and Chatbot 4U2 provides tailored TV recommendations through messaging interfaces based on user preference inputs. The Markov chain method models content prediction to predict viewing sequences so as to improve forecasting capabilities. The experimental analysis shows how FFM models that add TV content categories achieve superior performance than baseline NNMF models. Next-generation TV content recommendation benefits from content-based features since they demonstrate better accuracy than event-based and audience-based models which create an enhanced framework for TV content optimization and recommendation.
Ramagundam et al. (Wed,) studied this question.