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Netflix recommends : algorithms, film choice, and the history of taste /
"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 rec...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Oakland, California :
University of California Press,
[2021]
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 1 | 0 | |a Netflix recommends : |b algorithms, film choice, and the history of taste / |c Mattias Frey. |
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| 520 | |a "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"-- |c Provided by publisher. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | |a Description based on print version record. | ||
| 505 | 0 | |a 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. | |
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| 650 | 0 | |a Recommender systems (Information filtering) |x Social aspects. | |
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| 650 | 6 | |a Systèmes de recommandation (Filtrage d'information) |x Aspect social. | |
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