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Schrage, Michael: Recommendation Engines.

Authors :
Ryan, Erin
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]

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