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Recommendation Engines (The MIT Press Essential Knowledge series)
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About Recommendation Engines
Product Description 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? Review " 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 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, T he Innovator's Hypothesis: How Cheap Experiments Are Worth More than Good Ideas (MIT Press), and other books. Excerpt. © Reprinted by permission. All rights reserved. Recommendation engines transform human choice. Much as the steam engine energetically launched an industrial revolution, recommendation engines redefine insight and influence in an algorithmic age. Wherever choice matters, recommenders flourish. Better recommenders invariably mean better choices. Steam powers machines; recommenders empower people. They are the prime movers of their respective eras. They change how work gets done. That’s why Amazon, Alibaba, Google, Netflix, and TikTok are more than mere engines of commerce; they’re enablers of individual agency. Their platforms provide instant insight and personalized options to every single user they serve. Their algorithms deliver data-driven suggestions explicitly designed to inspire immediate exploration. Their relentless relevance produces confident curiosity. These recommenders literally—numerically, quantitatively—predict what “people like you”—and you in particular—might want or need. That compelling value proposition has infiltrated digital interactions worldwide. The results speak for themselves: recommender systems influence the video people watch, the books they read, the music they hear, the videogames they play, the investments they make, the friends they meet, the clothes they wear, the food they eat, the restaurants they frequent, the wine they drink, the vacations they take, the news they monitor, the exercises they do, the companions they woo, the products they buy, the cars they drive or hail, the routes they travel, the software they code, the slides they present, the email they send, the classes they take, the art they collect, the babysitters and handymen
