Challenges Experienced in Public Service Media Recommendation Systems

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.