Algorithmic logics of taste: Cultural taste and the Netflix recommender system

Algorithms are new cultural intermediaries (Bourdieu, 1984) that shape contemporary cultural experiences and identities. Their obscurity and complexity, however, hinder us from understanding their logics and processes as tastemakers. This research investigates the algorithmic logics of taste of the Netflix recommender system (NRS) to theorise the NRS’s construction of taste and workings as a cultural intermediary. I adapt Taina Bucher’s (2016) technography as a methodological approach in studying algorithms beyond the ‘black box’, through the analysis of discursive materials and traces of algorithmic interactions with key social actors. Findings reveal that the NRS constructs taste as rules—universal, definite and durable assumptions about cultural identities and objects. They are enacted through the algorithmic infrastructure and constrain human agency through predefined choices without adequate mechanisms for negotiation. This post-hegemonic power (Lash, 2007) contributes to the reproduction of dominant social structures through new ways to interpellate and codify social categories that are the basis of cultural taste. Through their logics, algorithms as cultural intermediaries transcend being capital translators (Hutchinson, 2017) and encompass cultural production, distribution and consumption. Their limitations and fallibilities, however, open pathways for rejecting and subverting the algorithmic construction of taste. The study presents theoretical and empirical contributions to research on algorithmic cultures and cultural taste, as well as methodological innovation in studying socio-technical actors. I acknowledge that research on algorithms is always partial and limited and thus, I prescribe directions for further research, with the intent to open a conversation on how algorithms can work better for/with humans