Enhanced User Experience in Digital TV: Recommending Content in Free-To-Air TV Contexts

Digital television (DTV) systems represent a major leap forward from analog ones, offering superior audio and video quality and interactive capabilities. The latter allow viewers to engage with broadcasts in a manner similar to navigating the Internet, opening up possibilities for new services such as recommendation systems and targeted advertising. However, even with such capabilities, free-to-air TV is still mostly offered according to the push paradigm, where the available content is just sent while neither required by users nor based on their preferences. The present proposal addresses this gap and introduces a personalized recommendation methodology for DTV, employing three distinct approaches: active search, background, and on-the-fly recommendation modes, thus leveraging the capabilities of modern DTV receivers, mobile devices, and centralized data repositories. Our system enhances user control over personal data by using a smartphone as the primary interface for user interaction and specialized data processing, thus keeping sensitive information within it. Consequently, this approach supports privacy and reduces the need for centralized processing, thereby lowering costs.