ZDF is a Public Service Media (PSM) broadcaster in Germany that uses recommender systems on its streaming service platform ZDFmediathek. One of the main use cases within the ZDFmediathek is Next Video, which is currently based on a Self-Attention based Sequential Recommendation model (SASRec). For this use case, we modified the loss function, the sampling method of negative items, and introduced the top-k negative sampling strategy and compared this to the vanilla SASRec model. We show that this not only reduces popularity bias, but also increases clicks and viewing volume compared to that of the vanilla version.

In the last few years, we have reportedly underlined the importance of the Public Service Media Remit for ZDF as a Public Service Media provider. Offering fair, diverse, and useful recommendations to users is just as important for us as being transparent about our understanding of these values, the metrics that we are using to evaluate their extent, and the algorithms in our system that produce such recommendations. This year, we have made a major step towards transparency of our algorithms and metrics describing them for a broader audience, offering the possibility for the audience to learn details about our systems and to provide direct feedback to us. Having the possibility to measure and track PSM metrics, we have started to improve our algorithms towards PSM values. In this work, we describe these steps and the results of actively debasing and adding exploration into our recommendations to achieve more fairness.

After multiple years of providing automated video recommendations in the ZDFmediathek, ZDF has established a solid ground for the usage of recommender systems. Being a Public Service Media (PSM) provider, our most important driver on this journey is our Public Service Media Remit (PSMR). We are committed to cultivate PSM values such as diversity, fairness, and transparency while providing fresh and relevant content. Therefore, it is important for us to not only measure the success of our recommender systems in terms of basic business Key Performance Indicators (KPIs) such as clicks and viewing minutes but also to ensure and to measure the achievement of PSM values. While speaking about PSM values, however, it is important to keep in mind that there is no easy way to directly measure values as such. In order to be able to measure their extent in a recommender system, we need to translate these values into public value metrics. However, not only the final results are essential for the PSMR. Additionally, it is highly important to establish transparency while working towards these results, that is, while defining the data, the algorithms, and the pipelines used in recommender systems. In our talk we will provide a deeper insight into how we approach this task with Model Cards and give an overview of some models, their Model Cards, and metrics that we are currently using for ZDFmediathek.

After multiple years of successfully applying recommendation algorithms at ZDF, a German Public Service Media provider, we have faced certain challenges in regards to the optimization of our systems and the resulting recommendations. The design and the optimization of our systems are guided by various, partially competing objectives and are, therefore, influenced by various factors. Similarly to commercial video on demand services, ZDF is interested in binding its audience by providing personalized recommendations in its streaming media service. However, more importantly, as a Public Service Media provider, we are committed to offer diverse, universal, unbiased, and transparent recommendations while following established editorial guidelines and strict privacy regulations. Additionally, we are committed to provide environmentally-friendly or green recommendations optimizing our systems for run time and power consumption. With the intent to start a public discussion, we describe the challenges that arise when optimizing Public Service Media recommendation systems towards machine learning metrics, business Key Performance Indicators, Public Service Media values, and run-time simultaneously, while aiming to keep the results transparent.