FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data. (arXiv:2212.06750v1 [cs.IR])

Today, recommender systems have played an increasingly important role in
shaping our experiences of digital environments and social interactions.
However, as recommender systems become ubiquitous in our society, recent years
have also witnessed significant fairness concerns for recommender systems.
Specifically, studies have shown that recommender systems may inherit or even
amplify biases from historical data, and as a result, provide unfair
recommendations. To address fairness risks in recommender systems, most of the
previous approaches to date are focused on modifying …

data fairness recommender systems systems