10.5281/zenodo.3404798
https://zenodo.org/records/3404798
oai:zenodo.org:3404798
Nixon, Lyndon
Lyndon
Nixon
0000-0001-7091-4543
MODUL Technology
Ciesielski, Krzysztof
Krzysztof
Ciesielski
Genistat AG
Philipp, Basil
Basil
Philipp
Genistat AG
AI for Audience Prediction and Profiling to Power Innovative TV Content Recommendation Services
Zenodo
2019
audience forecasting
prediction
predictive analytics
audience segmentation
viewer profiling
TV content recommendation
2019-09-11
10.5281/zenodo.3404797
https://zenodo.org/communities/retv-h2020
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
In contemporary TV audience prediction, outliers are considered mere anomalies in the otherwise cyclical trend and seasonality components that can be used to make predictions. In the ReTV project, we want to provide more accurate audience predictions in order to enable innovative services for TV content recommendation. This paper presents a concept for identifying the source of outliers and factoring TV content categories and the occurrence of events as additional features for training TV audience prediction. We show how this can improve the accuracy of the audience prediction. Finally, we outline how this work could also be combined with AI-enabled audience profiling to power new content recommendation services.
European Commission
10.13039/501100000780
780656
Enhancing and Re-Purposing TV Content for Trans-Vector Engagement