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Netflix recommendsalgorithms, film choice, and the history of taste /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Netflix recommendsMattias 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:	Recommender systems (Information filtering)Social aspects.
電子資源:	https://www.degruyter.com/isbn/9780520382022
ISBN:	9780520382022$q(electronic bk.)

Netflix recommendsalgorithms, 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$q(electronic bk.)

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

821135
Netflix (Firm)
Subjects--Topical Terms:

976328
Recommender systems (Information filtering)
--Social aspects.

LC Class. No.: HD9697.V544

Dewey Class. No.: 384.55/54

Netflix recommendsalgorithms, 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)
610 2 0 $a Netflix (Firm) $3 821135
650 0 $a Recommender systems (Information filtering) $x Social aspects. $3 976328
650 0 $a Streaming video $x Social aspects $z United States. $3 976329
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