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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Oakland, California :
University of California Press,
[2021]
|
| Schlagworte: | |
| Online-Zugang: | DE-862 DE-863 |
| Zusammenfassung: | "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"-- |
| Beschreibung: | 1 online resource |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9780520382022 0520382021 |
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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. | ||
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| contents | 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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| spelling | Frey, Mattias, author. Netflix recommends : algorithms, film choice, and the history of taste / Mattias Frey. Oakland, California : University of California Press, [2021] 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda "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"-- Provided by publisher. Includes bibliographical references and index. Description based on print version record. 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. In English. Netflix (Firm) http://id.loc.gov/authorities/names/nr2007013388 Netflix (Firm) fast Streaming video Social aspects United States. Recommender systems (Information filtering) Social aspects. Vidéo en continu Aspect social États-Unis. Systèmes de recommandation (Filtrage d'information) Aspect social. SOCIAL SCIENCE / Media Studies bisacsh United States fast has work: Netflix recommends (Text) https://id.oclc.org/worldcat/entity/E39PCFKqdrDmjKgrvthXhrQxym https://id.oclc.org/worldcat/ontology/hasWork Print version: Frey, Mattias. Netflix recommends Oakland, California : University of California Press, [2021] 9780520382381 (DLC) 2021006515 |
| spellingShingle | Frey, Mattias Netflix recommends : algorithms, film choice, and the history of taste / 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. Netflix (Firm) http://id.loc.gov/authorities/names/nr2007013388 Netflix (Firm) fast Streaming video Social aspects United States. Recommender systems (Information filtering) Social aspects. Vidéo en continu Aspect social États-Unis. Systèmes de recommandation (Filtrage d'information) Aspect social. SOCIAL SCIENCE / Media Studies bisacsh |
| subject_GND | http://id.loc.gov/authorities/names/nr2007013388 |
| title | Netflix recommends : algorithms, film choice, and the history of taste / |
| title_auth | Netflix recommends : algorithms, film choice, and the history of taste / |
| title_exact_search | Netflix recommends : algorithms, film choice, and the history of taste / |
| title_full | Netflix recommends : algorithms, film choice, and the history of taste / Mattias Frey. |
| title_fullStr | Netflix recommends : algorithms, film choice, and the history of taste / Mattias Frey. |
| title_full_unstemmed | Netflix recommends : algorithms, film choice, and the history of taste / Mattias Frey. |
| title_short | Netflix recommends : |
| title_sort | netflix recommends algorithms film choice and the history of taste |
| title_sub | algorithms, film choice, and the history of taste / |
| topic | Netflix (Firm) http://id.loc.gov/authorities/names/nr2007013388 Netflix (Firm) fast Streaming video Social aspects United States. Recommender systems (Information filtering) Social aspects. Vidéo en continu Aspect social États-Unis. Systèmes de recommandation (Filtrage d'information) Aspect social. SOCIAL SCIENCE / Media Studies bisacsh |
| topic_facet | Netflix (Firm) Streaming video Social aspects United States. Recommender systems (Information filtering) Social aspects. Vidéo en continu Aspect social États-Unis. Systèmes de recommandation (Filtrage d'information) Aspect social. SOCIAL SCIENCE / Media Studies United States |
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