Algorithmic and Editorial Diversity in Public Service Media: The Design Challenges

Publication: Research - peer-reviewJournal article

Abstract

With the growing influence of personalized algorithmic recommender systems on the exposure of media content to users, the relevance of discussing the diversity of recommendations increases, particularly as far as public service media (PSM) is concerned. An imagined implementation of a diversity diet system however triggers not only the classic discussion of the reach – distinctiveness balance for PSM, but also shows that ‘diversity’ is understood very differently in algorithmic recommender system communities than it is editorially and politically in the context of PSM. The design of a diversity diet system generates questions not just about editorial power, personal freedom and techno-paternalism, but also about the embedded politics of recommender systems as well as the human skills affiliated with PSM editorial work and the nature of PSM content.
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With the growing influence of personalized algorithmic recommender systems on the exposure of media content to users, the relevance of discussing the diversity of recommendations increases, particularly as far as public service media (PSM) is concerned. An imagined implementation of a diversity diet system however triggers not only the classic discussion of the reach – distinctiveness balance for PSM, but also shows that ‘diversity’ is understood very differently in algorithmic recommender system communities than it is editorially and politically in the context of PSM. The design of a diversity diet system generates questions not just about editorial power, personal freedom and techno-paternalism, but also about the embedded politics of recommender systems as well as the human skills affiliated with PSM editorial work and the nature of PSM content.
Original languageEnglish
JournalFirst Monday (Chicago)
ISSN1396-0466
StateSubmitted - 12 Jan 2017

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