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Descrizione libro Condizione: New. Codice articolo 40949140-n
Descrizione libro Soft Cover. Condizione: new. Codice articolo 9780262539074
Descrizione libro Condizione: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books!. Codice articolo OTF-S-9780262539074
Descrizione libro Condizione: New. Codice articolo ABLIING23Feb2215580084715
Descrizione libro Condizione: New. Book is in NEW condition. 0.61. Codice articolo 0262539071-2-1
Descrizione libro Condizione: New. New! This book is in the same immaculate condition as when it was published. Codice articolo 353-0262539071-new
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Descrizione libro Condizione: New. Codice articolo I-9780262539074
Descrizione libro Softcover. Condizione: New. 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?. Codice articolo DADAX0262539071
Descrizione libro PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo WB-9780262539074