One of the key talking points at this year’s RIPE conference – an event attended by industry and academia – was the issue of how public service broadcasters can continue to compete in a marketplace dominated by global players such as Netflix, Amazon Prime and Disney+. These are platforms with deep pockets (although a lot of that is debt), big-budget productions and subscriber bases that dwarf the PSBs’ own limited domestic reach.
Online media consumption has been radically transformed by how media companies algorithmically recommend content to their users. Public service media (PSM) have also realized the potential of recommender systems and are increasingly using these technologies to personalize their online offering. PSM are on the other hand required to disseminate diverse content, which can be incompatible with the logics of commercial recommender systems that primarily seek to drive up media consumption. Drawing on previous research on selective exposure and media diversity, this study presents the results from interviews with ten PSM informants across Europe, revealing that data scientists within these organizations are highly aware of the effects recommendations have on media consumption, and design the PSM online services accordingly. This study contributes with in-depth knowledge of how diversity has been interpreted at operational levels in PSM and how recommender systems are being adapted to a non-commercial setting.