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Title: Recommendations as Treatments
In recent years, a new line of research has taken an interventional view of recommender systems, where recommendations are viewed as actions that the system takes to have a desired effect. This interventional view has led to the development of counterfactual inference techniques for evaluating and optimizing recommendation policies. This article explains how these techniques enable unbiased offline evaluation and learning despite biased data, and how they can inform considerations of fairness and equity in recommender systems.  more » « less
Award ID(s):
1901168 2008139
PAR ID:
10379588
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
AI Magazine
Volume:
42
Issue:
3
ISSN:
0738-4602
Page Range / eLocation ID:
19 to 30
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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