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

By Blake Hallinan, Ted Striphas. New Media & Society, 2016

485 citations recorded by the source corpus.

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 (2006–2009), 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.”

Subfields: Computer Vision and Pattern Recognition, Safety Research, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/862d3b09a951fcdafa27e0435d48bf42795fc982

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Citations in the corpus

Managing visibility on YouTube through algorithmic gossip (2019, background): “The first investigates ‘technical’ sites, such as recommender systems and tech organisations, to examine how algorithms are constructed (e.g. Dourish, 2016; Hallinan and Striphas, 2016; Seaver, 2017; Vonderau, 2019).”
The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity (2019, background): “Algorithms reflect the norms and values of its makers and funders (Crawford 2013a; Hallinan and Striphas 2014; Jasanoff 2016).”
Anxiety, panic and self-optimization (2018, methodology): “Netflix properties such as Orange is the New Black and House of Cards were developed using audience information retrieved from data mining, ultimately influencing directorial, generic, casting and writing decisions (Hallinan and Striphas, 2016).”
Understanding User Beliefs About Algorithmic Curation in the Facebook News Feed (2015, background): “Hallinan and Striphas [14] call this the “court of algorithmic appeal” in which ideas are judged”
Algorithmic Cultures : Essays on Meaning, Performance and New Technologies (2016, background): “(Beer 2015; Beer and Burrows 2013; Gerlitz and Lury 2014; Grosser 2014; Hallinan and Striphas 2014; Hanrahan 2013; Marwick 2015; Lotan 2015) First, there are important questions to ask about how these measures are made and how they shape digital culture.”
Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability (2018, background): “, 2015), elite structuring masked as emergent sociality (Mackenzie, 2015; Striphas, 2015), automated media circulation (Hallinan and Striphas, 2014; Napoli, 2014), optimized labor (Kushner, 2013) and financial markets (Arnoldi, 2015), bracketed research methods (Driscoll and Walker, 2014), and ratio”
We get the algorithms of our ground truths: Designing referential databases in digital image processing (2017, background): “of studies: the first series of studies presents algorithms as powerful value-laden actors (Beer, 2009; Bucher, 2012; Gillespie, 2014; Hallinan and Striphas, 2016; Introna and Nissenbaum, 2000; Introna and Wood, 2004; Kraemer et al., 2011; Kushner, 2013; Steiner, 2012); the”
Algorithmic culture (2015, background): “How might he make sense of the entwining of culture, which he posited as a ‘court of human appeal’ (Williams, 1958: viii), and computational decision-making (see also Hallinan and Striphas, 2014)?”