Recommendation Engines (MIT Pr

Schrage, Michael

ISBN 10: 0262539071 / ISBN 13: 9780262539074

Edité par The MIT Press, 2020

Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc. N° de réf. du vendeur 00099393405

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?

À propos de l'auteur: 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.

Détails bibliographiques

Titre : Recommendation Engines (MIT Pr
Éditeur : The MIT Press
Date d'édition : 2020
Reliure : Couverture souple
Etat : Good