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Recommendation Engines

Recommendation Engines

Recommendation Engines
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Recommendation Engines Paperback - 2020

by Michael Schrage

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MIT Press, 2020. Paperback. Very Good. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.Dust jacket quality is not guaranteed.
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Details

  • Title Recommendation Engines
  • Author Michael Schrage
  • Binding Paperback
  • Condition Used - Very good
  • Pages 296
  • Volumes 1
  • Language ENG
  • Publisher MIT Press
  • Publication date 2020
  • Features Bibliography, Glossary, Index
  • Bookseller's Inventory # G0262539071I4N00
  • ISBN 9780262539074 / 0262539071
  • Weight 0.65 lbs (0.29 kg)
  • Dimensions 6.9 x 5 x 0.8 in (17.53 x 12.70 x 2.03 cm)
  • Category Technology & Industrial Arts
  • Library of Congress subjects Recommender systems (Information filtering)
  • Library of Congress Catalogue Number 2019042167
  • Dewey Decimal Code 025.04
  • Quantity available 4

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From the publisher

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?

About the author

Michael Schrage is a Research Fellow at the MIT Sloan School of Management's Initiative on the Digital Economy. A sought-after expert on innovation, design, and network effects, he is the author of Serious Play: How the World's Best Companies Simulate to Innovate, The Innovator's Hypothesis: How Cheap Experiments Are Worth More than Good Ideas (MIT Press), and other books.

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