Fairness in Recommendation Ranking through Pairwise Comparisons

2 Mar 2019Alex BeutelJilin ChenTulsee DoshiHai QianLi WeiYi WuLukasz HeldtZhe ZhaoLichan HongEd H. ChiCristos Goodrow

Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information. As such it is important to ask: what are the possible fairness risks, how can we quantify them, and how should we address them?.. (read more)

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