Recommendation engines (Record no. 32290)
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| 000 -LEADER | |
|---|---|
| fixed length control field | a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 231109b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780262539074 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 025.04 |
| Item number | SCH |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Schrage, Michael |
| 245 ## - TITLE STATEMENT | |
| Title | Recommendation engines |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc | MIT Press, |
| Date of publication, distribution, etc | 2020 |
| Place of publication, distribution, etc | Cambridge : |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xx, 275 p. ; |
| Other physical details | ill. |
| Dimensions | 18 cm |
| 365 ## - TRADE PRICE | |
| Price amount | 1300.00 |
| Price type code | INR |
| Unit of pricing | 01 |
| 490 ## - SERIES STATEMENT | |
| Series statement | The MIT Press essential knowledge series |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Internet Search Engines |
| Topical term or geographic name as entry element | Recommender systems |
| Topical term or geographic name as entry element | Information filtering |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Item type | Books |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Cost, normal purchase price | Total Checkouts | Full call number | Barcode | Date last seen | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | DAU | DAU | 08/11/2023 | 1300.00 | 025.04 SCH | 034467 | 09/11/2023 | Books |