Notice bibliographique
- Notice
Type(s) de contenu et mode(s) de consultation : Texte noté : sans médiation
Auteur(s) : Frey, Mattias
Titre(s) : Netflix recommends [Texte imprimé] : algorithms, film choice, and the history of taste / Mattias Frey
Publication : Oakland (Calif.) : University of California press, copyright 2021
Description matérielle : 1 vol. (viii-273 p.) : ill. ; 23 cm
Note(s) : Includes bibliographical references (p. 257-267) and index
"Algorithmic 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 are novel, effective, and widely used
methods to choose films and series. 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
alarming, neither as trusted nor widely used, as their celebrants and critics maintain.
Netflix Recommends illustrates 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"
Sujet(s) : Vidéos sur Internet -- Société -- États-Unis
Systèmes de recommandation (informatique) -- Société -- États-Unis
Netflix
Indice(s) Dewey :
384.555 4 (23e éd.) = Télévision par abonnement
Identifiants, prix et caractéristiques : ISBN 9780520382381 (erroné). - ISBN 0520382382 (erroné). - ISBN 9780520382046. - ISBN
0520382048. - ISBN 9780520382022 (erroné)
Identifiant de la notice : ark:/12148/cb46965630w
Notice n° :
FRBNF46965630
(notice reprise d'un réservoir extérieur)
Table des matières : Introduction ; Why we need film and series suggestions ; How algorithmic recommender
systems work ; Cracking the code, part I : developing Netflix's recommendation algorithms
; Cracking the code, part II : unpacking Netflix's myth of big data ; How real people
choose films and series ; Afterword : robot critics vs. human experts ; Appendix
: designing the empirical audience study.