The Mutual Domestication of Users and Algorithmic Recommendations on Netflix

Abstract

This article examines the mutual domestication of users and recommendation algorithms on Netflix. Based on 25 interviews with users and an inductive analysis of their practices and profiles on the platform, we discuss five dynamics through which this mutual domestication occurs: personalization, or the ways in which individualized relationships between users and the platform are built; how algorithmic recommendations are integrated into a matrix of cultural codes; the rituals through which they are incorporated into spatial and temporal processes in daily life; the resistance to various aspects of Netflix as a form to enact agency; and the conversion or transformation of the private consumption of the platform into a public issue. The conclusion elaborates on the theoretical and analytical implications of this approach, to rethink the relationship between algorithms and culture.