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Schrage, Michael: Recommendation Engines.
- Source :
-
Preservation, Digital Technology & Culture . Dec2020, Vol. 49 Issue 4, p183-184. 2p. - Publication Year :
- 2020
-
Abstract
- Schrage then covers various "recommender approaches" (120), including simple ones like "Most Popular" (an easy way to deal with the "cold start" problem: how to draw in new users for whom the system has insufficient data [120]). Whether you find these ubiquitous suggestions helpful, benign, annoying, or worse, I Recommendation Engines i , by innovation researcher Michael Schrage, makes an interesting read for anyone who wants to understand how companies use data about you in order to fuel such predictions. [Extracted from the article]
- Subjects :
- *RECOMMENDER systems
*ENGINES
*HUMAN behavior
Subjects
Details
- Language :
- English
- ISSN :
- 21952957
- Volume :
- 49
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Preservation, Digital Technology & Culture
- Publication Type :
- Review
- Accession number :
- 151228374
- Full Text :
- https://doi.org/10.1515/pdtc-2021-0004