What is it about?
In the web, rankings should be useful to the consumer, but should also give fair exposure to the item producers. We introduce a mathematical object and algorithms that enables us to do just that: efficiently find rankings that are as fair and useful as possible.
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Why is it important?
Our contributions bring us a step closer to having algorithms deployable on large scale, that can be used to prevent unfairness in rankings, without too much decreasing the utility of the consumers.
Perspectives
The introduction of the expohedron is a first step and I hope a lot of useful ranking techniques could be derived using it.
Till Kletti
NaverLabs Europe
Read the Original
This page is a summary of: Introducing the Expohedron for Efficient Pareto-optimal Fairness-Utility Amortizations in Repeated Rankings, February 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3488560.3498490.
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