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Netflix recommends = algorithms, film choice, and the history of taste /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Netflix recommends/ Mattias Frey.
其他題名:	algorithms, film choice, and the history of taste /
作者:	Frey, Mattias.
出版者:	Oakland, CA :University of California Press, : c2021.,
面頁冊數:	1 online resource (282 p.)
標題:	Streaming video - Social aspects - United States. -
電子資源:	https://www.degruyter.com/isbn/9780520382022
ISBN:	9780520382022

Netflix recommends = algorithms, film choice, and the history of taste /
Frey, Mattias.

Netflix recommendsalgorithms, film choice, and the history of taste /[electronic resource] :Mattias Frey. - 1st ed. - Oakland, CA :University of California Press,c2021. - 1 online resource (282 p.)

Includes bibliographical references and index.

Frontmatter --

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 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.

Mode of access: Internet via World Wide Web.

In English.

ISBN: 9780520382022

Standard No.: 10.1525/9780520382022doiSubjects--Corporate Names:

1207534
Netflix (Firm)
Subjects--Topical Terms:

1457909
Streaming video
--Social aspects--United States.

LC Class. No.: HD9697.V544

Dewey Class. No.: 384.55/54

Netflix recommends = algorithms, film choice, and the history of taste /
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300 $a 1 online resource (282 p.)
504 $a Includes bibliographical references and index.
505 0 0 $t Frontmatter -- $t Contents -- $t Acknowledgments -- $t Introduction -- $t 1 Why We Need Film and Series Suggestions -- $t 2 How Algorithmic Recommender Systems Work -- $t 3 Developing Netflix's Recommendation Algorithms -- $t 4 Unpacking Netflix's Myth of Big Data -- $t 5 How Real People Choose Films and Series -- $t Afterword: Robot Critics vs. Human Experts -- $t Appendix: Designing the Empirical Audience Study -- $t Notes -- $t Selected Bibliography -- $t Index.
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 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.
538 $a Mode of access: Internet via World Wide Web.
546 $a In English.
588 $a Description based on online resource; title from PDF title page (publisher's Web site, viewed 01. Dez 2022)
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650 0 $a Streaming video $x Social aspects $z United States. $3 1457909
650 0 $a Recommender systems (Information filtering) $x Social aspects. $3 1457908
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