Data-driven personalisation of television content: a survey

Nixon, Lyndon; Foss, Jeremy; Apostolidis, Konstantinos; Mezaris, Vasileios

doi:10.1007/s00530-022-00926-6

Cited by 3 publications

References 253 publications

(299 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

AI and data-driven media analysis of TV content for optimised digital content marketing

Nixon,

Apostolidis,

Apostolidis

et al. 2024

Multimedia Systems

View full text Add to dashboard Cite

To optimise digital content marketing for broadcasters, the Horizon 2020 funded ReTV project developed an end-to-end process termed “Trans-Vector Publishing” and made it accessible through a Web-based tool termed “Content Wizard”. This paper presents this tool with a focus on each of the innovations in data and AI-driven media analysis to address each key step in the digital content marketing workflow: topic selection, content search and video summarisation. First, we use predictive analytics over online data to identify topics the target audience will give the most attention to at a future time. Second, we use neural networks and embeddings to find the video asset closest in content to the identified topic. Third, we use a GAN to create an optimally summarised form of that video for publication, e.g. on social networks. The result is a new and innovative digital content marketing workflow which meets the needs of media organisations in this age of interactive online media where content is transient, malleable and ubiquitous.

show abstract