Netflix Recommends: Algorithms, Film Choice, and the History of TasteAlgorithmic recommender systems, deployed by media companies to suggest content based on users’ viewing histories, have inspired hopes for personalized, curated media but also dire warnings of filter bubbles and media homogeneity. Curiously, both proponents and detractors assume that recommender systems for choosing films and series are novel, effective, and widely used. Scrutinizing the world’s most subscribed streaming service, Netflix, this book challenges that consensus. Investigating real-life users, marketing rhetoric, technical processes, business models, and historical antecedents, Mattias Frey demonstrates that these choice aids are neither as revolutionary nor as alarming as their celebrants and critics maintain—and neither as trusted nor as widely used. Netflix Recommends brings to light the constellations of sources that real viewers use to choose films and series in the digital age and argues that although some lament AI’s hostile takeover of humanistic cultures, the thirst for filters, curators, and critics is stronger than ever. |
Contents
Why We Need Film and Series Suggestions | 23 |
How Algorithmic Recommender Systems Work | 38 |
Unpacking Netflixs Myth of Big Data | 96 |
Designing the Empirical Audience Study | 209 |
Notes | 217 |
Selected Bibliography | 257 |
Other editions - View all
Netflix Recommends: Algorithms, Film Choice, and the History of Taste Mattias Frey Limited preview - 2021 |
Netflix Recommends: Algorithms, Film Choice, and the History of Taste Mattias Frey Limited preview - 2021 |
Common terms and phrases
2nd rev aggregators Algorithmic Culture algorithmic recommender systems audience audiovisual automation behaviors Big Data Bracha Shapira British Film Institute Cambridge choice cinema Cinematch collaborative filtering company’s consumers consumption credibility curation Demand platforms Digital Age European Audiovisual Observatory Evaluation example experience Facebook film critics film or series films and series forms Francesco Ricci friends functions genre human individual industry interface interviews Jennifer Holt Journal Kevin McDonald Lior Rokach marketing Metacritic MUBI Netflix Prize Netflix Recommender System OfCom overall Participant percent personalization popular predict ratings real users recommendation engine Recommender Systems Handbook rhetoric Rotten Tomatoes Sarandos selection social media streaming Striphas subscribers suggestions survey SVOD taste television trailers trust University Press Unweighted base Video on Demand viewers viewing VOD platforms VOD recommender systems VOD services watch films wisdom of crowds word of mouth York YouTube