The New Era: AI-Powered Highlights and Personalization

Abstract

Artificial Intelligence (AI) is revolutionizing the sports media landscape by offering a number of features such as automated highlight generation, smart content targeting, and much more engagement on digital platforms. In the traditional sports broadcasting scenario, manual editing of content and limited customization of sports delivery exist, and this approach might lead to customized content that is less attractive towards the end of the game. There are multiple moments that can be captured and then embedded in a real-time highlight-sheet made possible by machine learning technologies powered by computer vision, natural language processing (NLP), and deep learning, as well as recommender systems. The advancement of computer vision, natural language processing (NLP), deep learning, and recommender systems now makes it possible to develop smart solutions that can identify the most important moments in digital video streams and automate feedback-based, real-time Bundesliga highlights and video playlists according to user behavior and preferences. In the current paper, the application of automation in the sports broadcasting sector is reflected, and a conceptual fusion of automated Event Detection, Event Highlighting, and OTT (Over The Top) and Social Media Recommendation systems is proposed. The research analysis in the study shows that an AI workflow can save hundreds, if not thousands, of hours and significantly increase user satisfaction, viewability, accuracy of recommendations, and more when it comes to producing content. The study also discusses some new challenges arising, such as information security, algorithmic bias, computational problems, and ethical issues related to personalized learning with Artificial Intelligence (AI). Finally, future research directions towards multimodal foundation models, edge AI, and explainable recommendation systems, as well as generative AI, are stressed for the next-generation sports intelligent broadcast system.