Recommended for you: The Netflix Prize and the production of algorithmic culture

Abstract

How does algorithmic information processing affect the meaning of the word culture, and, by extension, cultural practice? We address this question by focusing on the Netflix Prize (2006)(2007)(2008)(2009), a contest offering US$1m to the first individual or team to boost the accuracy of the company's existing movie recommendation system by 10%. Although billed as a technical challenge intended for engineers, we argue that the Netflix Prize was equally an effort to reinterpret the meaning of culture in ways that overlapped with, but also diverged in important respects from, the three dominant senses of the term assayed by Raymond Williams. Thus, this essay explores the conceptual and semantic work required to render algorithmic information processing systems legible as forms of cultural decision making. It also then represents an effort to add depth and dimension to the concept of "algorithmic culture." Keywords Algorithm, algorithmic culture, culture, data, movies, Netflix, ratings, Raymond Williams, recommendation, taste "Connect[ing] people to movies they love" is the Netflix company mantra (Netflix Prize, n.d.). It frames the firm as a cheery mediator of people and movies, one that produces delight (and, of course, profit) by fusing technology and subscriber information in a complex alchemy of audiovisual matchmaking. While not inaccurate, such a move mystifies the semantic and socio-technical processes by which these connections are made,

Algorithmic IF … THEN rules and the conditions and consequences of power

“…For example, Kushner (2013Kushner ( , p. 1242 suggests that in the case of translation, algorithms need human "help" in order to become a "freelance translation machine, an assemblage of circuits and flesh that transforms text from one language to another with a computer's efficiency and the sensitivity of the human mind." Furthermore, Hallinan and Striphas (2014) argue, in their study of an online recommendation system, that the algorithm cannot work with various oddities in customer preference and instead of being resolved, these oddities need to be worked around. In these accounts, not only is the algorithm made accessible to research, it is also decentred in its agential consequences.…”

The dynamics and potentials of big data for audience research

“…This claim to alchemical mastery consciously 'mystifies the semantic and socio-technical processes by which these connections are made' (Hallinan and Striphas, 2016). In part, this procedural mystification stems from the fact that the algorithmic engines of the major collectors of user data are deeply proprietary and effectively 'wired-shut' from public scrutiny.…”

“…As a case study in 'algorithmic culture', Hallinan and Striphas (2016) describe the case of Netflix: 'a cheery mediator of people and movies, one that produces delight (and, of course, profit) by fusing technology and subscriber information in a complex alchemy of audiovisual matchmaking ' (p. 117). This claim to alchemical mastery consciously 'mystifies the semantic and socio-technical processes by which these connections are made' (Hallinan and Striphas, 2016).…”

Technologies of attribution: characterizing the citizen-consumer in surveillance performance

“…I want to know my future" (Scourti 2012b). This string of 9 Ted Striphas' work helps to conceptualize this dialogue further (Hallinan and Striphas 2014;Striphas 2012;Granieri 2014). His term "algorithmic culture" describes the ways in which algorithms increasingly do traditional "cultural" work: "of the sorting, classifying, and hierarchizing of people, places, objects, and ideas" (Granieri 2014).…”