Public Service Media, Diversity and Algorithmic Recommendation: Tensions between Editorial Principles and Algorithms in European PSM Organizations

Public Service Media (PSM) websites are an interesting case for the implementation of recommender systems for media personalization, as the PSM organizations need to balance the optimization of exposure with traditional but ill-defined PSM policy goals such as fairness, viewpoint diversity and transparency. Furthermore, the mathematical logic of recommender system needs to be adapted to the legacy broadcasting scheduling and publishing strategies and procedures. Finally, as the PSM organizations step into new territories, domestication and adaption of the recommender system technologies must take place while PSM organizations try to embrace the new knowledge and new professions associated with recommender systems. Based on 25 in-depth interviews conducted from December 2016 to April 2019, this paper presents a cross-European analysis of the implementation of recommender systems in nine European public service media organizations from eight countries. The findings indicate that PSM organizations, although viewing personalisation as competitive necessity, approach recommendation systems with hesitation in order to maintain core PSM-values in the online environment. Furthermore, although the collaborative filtering chosen by the PSM organizations indicate a usercentered approach, curation systems on top of recommender systems re-install a broadcaster-centric approach.