Abstract
In today’s online world, bias is ubiquitous. In a Video-on-Demand (VOD) context, there are two main sources for bias: (1) content providers and their methods that generate recommendations; (2) user preferences. We use real world data from one of the largest German Public Service Media (PSM) platforms to study both possible causes by examining content recommendations and content consumption (classified as Education, Information, Culture, and Entertainment according to the regulations governing PSM operations). We find that Education and Entertainment content to be recommended disproportionately. Algorithmic personalized recommendations lead to a bias towards both Education and Entertainment for the recommendations. Editorial content recommendations favor Entertainment content. Additionally, we find that the bias introduced by the recommender systems to be larger than the one introduced by the viewer. We recommend testing an increased use of algorithmic recommendations as these might lead to a lower bias towards Entertainment content.
Paper Type
Short Paper
DOI
10.62036/ISD.2025.140
Let Me Entertain You: On the Bias of Editorial and Algorithmic Recommendations in Public Service Media
In today’s online world, bias is ubiquitous. In a Video-on-Demand (VOD) context, there are two main sources for bias: (1) content providers and their methods that generate recommendations; (2) user preferences. We use real world data from one of the largest German Public Service Media (PSM) platforms to study both possible causes by examining content recommendations and content consumption (classified as Education, Information, Culture, and Entertainment according to the regulations governing PSM operations). We find that Education and Entertainment content to be recommended disproportionately. Algorithmic personalized recommendations lead to a bias towards both Education and Entertainment for the recommendations. Editorial content recommendations favor Entertainment content. Additionally, we find that the bias introduced by the recommender systems to be larger than the one introduced by the viewer. We recommend testing an increased use of algorithmic recommendations as these might lead to a lower bias towards Entertainment content.
Recommended Citation
de Zoeten, M., Hauck, M., Briesch, M., Sobania, D. & Rothlauf, F. (2025). Let Me Entertain You: On the Bias of Editorial and Algorithmic Recommendations in Public Service MediaIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.140