Recommendation engines / Michael Schrage.
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| Location | Call Number / Copy Notes | Barcode | Shelving Location | Holdable? | Status | Due Date |
|---|---|---|---|---|---|---|
| Valencia Main Library | ZA3084 .S37 2020 | 37684001092566 | Valencia Stacks | Copy hold / Volume hold | Available | - |
Record details
- ISBN: 9780262539074
- ISBN: 0262539071
- Physical Description: xx, 275 pages : illustrations (black and white) ; ... Read More
- Publisher: Cambridge, Massachusetts : The MIT Press, [2020]
- Copyright: ©2020
Content descriptions
| Bibliography, etc. Note: | Includes bibliographical references and index. |
| Formatted Contents Note: | What recommenders are/ Why recommenders matter -- ... Read More |
| Summary, etc.: | "How does Netflix know just what to suggest you ... Read More |
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| Subject: | Recommender systems (Information filtering) |
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|---|---|---|---|
| 001 | 180475 | ||
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| 100 | 1 | . | ‡aSchrage, Michael. ‡4aut |
| 245 | 1 | 0. | ‡aRecommendation engines / ‡cMichael Schrage. |
| 264 | 1. | ‡aCambridge, Massachusetts : ‡bThe MIT Press, ‡c[2020] | |
| 264 | 4. | ‡c©2020 | |
| 300 | . | ‡axx, 275 pages : ‡billustrations (black and white) ; ‡c18 cm. | |
| 336 | . | ‡atext ‡btxt ‡2rdacontent | |
| 337 | . | ‡aunmediated ‡bn ‡2rdamedia | |
| 338 | . | ‡avolume ‡bnc ‡2rdacarrier | |
| 490 | 1 | . | ‡aThe MIT Press essential knowledge series |
| 504 | . | ‡aIncludes bibliographical references and index. | |
| 520 | . | ‡a"How does Netflix know just what to suggest you watch next? How does Amazon determine what a "customer like you" has also purchased? The answer is recommender systems, the technological concept that lies at the heart of most of the successful companies in the digital economy. Michael Schrage starts with the origins of recommender systems, which go back further than you think (see: the Oracle at Delphi for one of history's earliest recommenders), and a history of the first companies to harness recommendations. He then discusses the technology behind how recommenders work: the AI and machine learning algorithms that power these recommender platforms. Next he discusses the role of user experience, and how recommender systems are designed, and how design choices function as nudges to make certain recommendations more salient than others. He explores three case studies: Spotify, Bytedance, and Stitch Fix, looking at how recommenders can create new business solutions and how algorithms can go beyond curation to content creation. The concluding chapter on the future of recommender systems is perhaps the most enlightening. Moving away from technology and business, Schrage embraces the philosophical, probing the role of free will in a world mediated by recommender systems (a recommendation inherently offers a choice; without the element of choice, any digital manipulation of our preferences cannot truly be called a "recommendation"), and exploring the role of recommender systems as a means of improving the self. In the vein of Free Will, this book presents the essential information while revealing the author's point of view. Schrage wants to push our understanding of recommender systems beyond the technological, to understand what societal role they play and what opportunities they offer now and in the future"-- ‡cProvided by publisher. | |
| 505 | 0 | . | ‡aWhat recommenders are/ Why recommenders matter -- On the origins of recommendation -- A brief history of recommendation engines -- How recommenders work -- Experiencing recommendations -- Recommendation innovators -- The recommender future. |
| 650 | 0. | ‡aRecommender systems (Information filtering) | |
| 830 | 0. | ‡aMIT Press essential knowledge series. | |
| 994 | . | ‡aC0 ‡bBKC | |
| 905 | . | ‡utlin2@berklee.edu | |
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