Recommendations as Treatments
DOI:
https://doi.org/10.1609/aimag.v42i3.18141Abstract
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.
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Published
2021-11-20
How to Cite
Joachims, T., London, B., Su, Y., Swaminathan, A., & Wang, L. (2021). Recommendations as Treatments. AI Magazine, 42(3), 19-30. https://doi.org/10.1609/aimag.v42i3.18141
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