Acerca de este artículo

Product Identifiers

PublisherMIT Press

ISBN-100262539071

ISBN-139780262539074

eBay Product ID (ePID)15050027611

Product Key Features

Book TitleRecommendation Engines

Number of Pages296 Pages

LanguageEnglish

Publication Year2020

TopicSocial Aspects, Free Will & Determinism, Web / Search Engines

IllustratorYes

GenreComputers, Philosophy, Technology & Engineering

AuthorMichael Schrage

Book SeriesThe MIT Press Essential Knowledge Ser.

FormatTrade Paperback

Dimensions

Item Height0.8 in

Item Weight9.7 Oz

Item Length6.9 in

Item Width5 in

Additional Product Features

Intended AudienceTrade

LCCN2019-042167

Reviews" Recommendation Engines is an eye-opener to readers who [...] find the ubiquitous "what people like you bought" suggestions of online merchants faintly intrusive and only occasionally useful." -- Strategy and Business, Recommendation Engines is an eye-opener to readers who [...] find the ubiquitous "what people like you bought" suggestions of online merchants faintly intrusive and only occasionally useful. -- Strategy and Business

Table Of ContentSeries Foreword vii Introduction ix 1 What Recommenders Are/Why Recommenders Matter 1 2 On the Origins of Recommendation 35 3 A Brief History of Recommendation Engines 63 4 How Recommenders Work 109 5 Experiencing Recommendations 149 6 Recommendation Innovators 177 7 The Recommender Future 211 Acknowledgments 241 Glossary 245 Notes 251 Further Reading 261 Index 263

SynopsisHow companies like Amazon and Netflix know what "you might also like" the history, technology, business, and social impact of online recommendation engines. Increasingly, our technologies are giving us better, faster, smarter, and more personal advice than our own families and best friends. Amazon already knows what kind of books and household goods you like and is more than eager to recommend more; YouTube and TikTok always have another video lined up to show you; Netflix has crunched the numbers of your viewing habits to suggest whole genres that you would enjoy. In this volume in the MIT Press's Essential Knowledge series, innovation expert Michael Schrage explains the origins, technologies, business applications, and increasing societal impact of recommendation engines, the systems that allow companies worldwide to know what products, services, and experiences "you might also like." Schrage offers a history of recommendation that reaches back to antiquity's oracles and astrologers; recounts the academic origins and commercial evolution of recommendation engines; explains how these systems work, discussing key mathematical insights, including the impact of machine learning and deep learning algorithms; and highlights user experience design challenges. He offers brief but incisive case studies of the digital music service Spotify; ByteDance, the owner of TikTok; and the online personal stylist Stitch Fix. Finally, Schrage considers the future of technological recommenders: Will they leave us disappointed and dependent--or will they help us discover the world and ourselves in novel and serendipitous ways?, How companies like Amazon and Netflix know what "you might also like"- the history, technology, business, and social impact of online recommendation engines. Increasingly, our technologies are giving us better, faster, smarter, and more personal advice than our own families and best friends. Amazon already knows what kind of books and household goods you like and is more than eager to recommend more; YouTube and TikTok always have another video lined up to show you; Netflix has crunched the numbers of your viewing habits to suggest whole genres that you would enjoy. In this volume in the MIT Press's Essential Knowledge series, innovation expert Michael Schrage explains the origins, technologies, business applications, and increasing societal impact of recommendation engines, the systems that allow companies worldwide to know what products, services, and experiences "you might also like." Schrage offers a history of recommendation that reaches back to antiquity's oracles and astrologers; recounts the academic origins and commercial evolution of recommendation engines; explains how these systems work, discussing key mathematical insights, including the impact of machine learning and deep learning algorithms; and highlights user experience design challenges. He offers brief but incisive case studies of the digital music service Spotify; ByteDance, the owner of TikTok; and the online personal stylist Stitch Fix. Finally, Schrage considers the future of technological recommenders- Will they leave us disappointed and dependent-or will they help us discover the world and ourselves in novel and serendipitous ways?, "How does Netflix know just what to suggest you watch next? How does Amazon determine what a "customer like you" has also purchased? The answer is recommender systems, the technological concept that lies at the heart of most of the successful companies in the digital economy. Michael Schrage starts with the origins of recommender systems, which go back further than you think (see: the Oracle at Delphi for one of history's earliest recommenders), and a history of the first companies to harness recommendations. He then discusses the technology behind how recommenders work: the AI and machine learning algorithms that power these recommender platforms. Next he discusses the role of user experience, and how recommender systems are designed, and how design choices function as nudges to make certain recommendations more salient than others. He explores three case studies: Spotify, Bytedance, and Stitch Fix, looking at how recommenders can create new business solutions and how algorithms can go beyond curation to content creation. The concluding chapter on the future of recommender systems is perhaps the most enlightening. Moving away from technology and business, Schrage embraces the philosophical, probing the role of free will in a world mediated by recommender systems (a recommendation inherently offers a choice; without the element of choice, any digital manipulation of our preferences cannot truly be called a "recommendation"), and exploring the role of recommender systems as a means of improving the self. In the vein of Free Will, this book presents the essential information while revealing the author's point of view. Schrage wants to push our understanding of recommender systems beyond the technological, to understand what societal role they play and what opportunities they offer now and in the future"--, How companies like Amazon, Netflix, and Spotify know what "you might also like": the history, technology, business, and societal impact of online recommendation engines. Increasingly, our technologies are giving us better, faster, smarter, and more personal advice than our own families and best friends. Amazon already knows what kind of books and household goods you like and is more than eager to recommend more; YouTube and TikTok always have another video lined up to show you; Netflix has crunched the numbers of your viewing habits to suggest whole genres that you would enjoy. In this volume in the MIT Press's Essential Knowledge series, innovation expert Michael Schrage explains the origins, technologies, business applications, and increasing societal impact of recommendation engines, the systems that allow companies worldwide to know what products, services, and experiences "you might also like."

LC Classification NumberZA3084.S37 2020