Recommendations as Treatments

Thorsten Joachims, Ben London, Yi Su, Adith Swaminathan, Lequn Wang
<span title="2021-11-20">2021</span> <i title="Association for the Advancement of Artificial Intelligence (AAAI)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/27wksbinzzhjfow2wuy6m2iefm" style="color: black;">The AI Magazine</a> </i> &nbsp;
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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aimag.v42i3.18141">doi:10.1609/aimag.v42i3.18141</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hdyi4nadijgp3fpieqojib5pfq">fatcat:hdyi4nadijgp3fpieqojib5pfq</a> </span>
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