.jpg)
Tech Papers 2019: This paper presents a machine learning approach to adapt TV user interfaces by analysing behavioural patterns, enabling truly personalised and adaptive viewing experiences beyond traditional remote or voice-based customisation.
Abstract
Currently, user interfaces displayed to viewers on their TVs look and behave similarly for all users. While sometimes it is possible to customise the user interface (UI) to a degree, users will rarely experience true customisation on a TV, mainly because that is difficult using a remote control or voice commands. Our research focuses on utilising machine learning to discover and interpret behavioural patterns and to adapt the UI accordingly. In this paper, we will share our solution for a truly adaptive UI, tailored to each viewer. This paper also showcases the machine learning engine, and examines our behavioural mapping technique and the mathematical theory behind it.
Exclusive Content
This article is available with a Technical Paper Pass
Dynamic streaming content packaging with C2PA
Tech Papers 2026: This paper presents an implementation of the approach adopted by C2PA for live video to dynamic packaging.
.jpg)
Dynamic power control for sustainable broadcast transmitter networks
Tech Papers 2026: This paper proposes an approach that uses predictive modelling in combination with real-time interference monitoring to optimise transmitter powers dynamically, with minimal impact on the consumer.
.jpg)
A standardised framework for C2PA provenance in media workflows
Tech Papers 2026: This paper presents the first standardised framework for implementing C2PA for media provenance across newsrooms of varying sizes and operational contexts.
.jpg)
Building MXL together: Progress of the multi-vendor open-source media exchange SDK
Tech Papers 2026: This paper presents the Media eXchange Layer (MXL), an open-source SDK project hosted by the Linux Foundation in collaboration with the EBU and NABA.
.jpg)
Search first, inspect visually when needed: A multi-agent architecture for semantic video archival retrieval
Tech Papers 2026: This paper introduces Smart Chat, a multi-agent video question-answering system that searches indexed video moments, localizes candidate evidence, and inspects a short clip only when visual verification is needed.