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Recommendation Engines Paperback Michael Schrage
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Recommendation Engines Paperback Michael Schrage
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Recommendation Engines Paperback Michael Schrage

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    Item specifics

    Condition
    Good
    A book that has been read but is in good condition. Very minimal damage to the cover including scuff marks, but no holes or tears. The dust jacket for hard covers may not be included. Binding has minimal wear. The majority of pages are undamaged with minimal creasing or tearing, minimal pencil underlining of text, no highlighting of text, no writing in margins. No missing pages. See all condition definitionsopens in a new window or tab
    Seller Notes
    “Used book that is in clean, average condition without any missing pages. 100% Money-Back Guarantee.”
    Publication Name
    MIT Press
    ISBN
    9780262539074
    Category

    About this product

    Product Identifiers

    Publisher
    MIT Press
    ISBN-10
    0262539071
    ISBN-13
    9780262539074
    eBay Product ID (ePID)
    15050027611

    Product Key Features

    Book Title
    Recommendation Engines
    Number of Pages
    296 Pages
    Language
    English
    Publication Year
    2020
    Topic
    Social Aspects, Free Will & Determinism, Web / Search Engines
    Illustrator
    Yes
    Genre
    Computers, Philosophy, Technology & Engineering
    Author
    Michael Schrage
    Book Series
    The MIT Press Essential Knowledge Ser.
    Format
    Trade Paperback

    Dimensions

    Item Height
    0.8 in
    Item Weight
    9.7 Oz
    Item Length
    6.9 in
    Item Width
    5 in

    Additional Product Features

    Intended Audience
    Trade
    LCCN
    2019-042167
    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 Content
    Series 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
    Synopsis
    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 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 Number
    ZA3084.S37 2020

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