Recommendation Engines

Schrage, Michael

Recommendation Engines
  • 出版商: MIT
  • 出版日期: 2020-09-01
  • 售價: $720
  • 貴賓價: 9.5 折 $684
  • 語言: 英文
  • 頁數: 296
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 0262539071
  • ISBN-13: 9780262539074

    • 立即出貨(限量) (庫存=1)

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商品描述

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?

商品描述(中文翻譯)

如何像亞馬遜和Netflix這樣的公司知道「你可能也喜歡什麼」：在線推薦引擎的歷史、技術、商業及社會影響。

隨著科技的進步，我們的技術提供的建議越來越好、越來越快、越來越智能，甚至比我們的家人和摯友更具個人化。亞馬遜已經知道你喜歡什麼類型的書籍和家居用品，並熱衷於推薦更多；YouTube和TikTok總是準備好下一個視頻來展示給你；Netflix則通過分析你的觀看習慣，建議你可能會喜歡的整個類型。在這本MIT Press的《Essential Knowledge》系列中，創新專家Michael Schrage解釋了推薦引擎的起源、技術、商業應用及其日益增長的社會影響，這些系統使全球公司能夠了解「你可能也喜歡」的產品、服務和體驗。

Schrage回顧了推薦的歷史，追溯到古代的神諭和占星師；敘述了推薦引擎的學術起源和商業演變；解釋了這些系統的運作方式，討論了關鍵的數學見解，包括機器學習和深度學習算法的影響；並突顯了用戶體驗設計的挑戰。他提供了數位音樂服務Spotify、TikTok的擁有者ByteDance以及在線個人造型師Stitch Fix的簡短但深刻的案例研究。最後，Schrage考慮了技術推薦系統的未來：它們會讓我們感到失望和依賴，還是會幫助我們以新穎和意外的方式發現世界和自我？

作者簡介

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.

作者簡介(中文翻譯)

邁克爾·施拉基（Michael Schrage）是麻省理工學院斯隆管理學院數位經濟倡議的研究員。他是創新、設計和網絡效應方面的知名專家，著有《嚴肅的遊戲：世界最佳公司如何模擬以創新》（Serious Play: How the World's Best Companies Simulate to Innovate）、《創新者的假設：廉價實驗的價值超過好點子》（The Innovator's Hypothesis: How Cheap Experiments Are Worth More than Good Ideas）（麻省理工學院出版社）及其他書籍。

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