  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 either the existing
training data samples or the deployed recommender algorithms, but unfortunately
with limited degrees of success. In this paper, we propose a new approach
called fair recommendation with optimized antidote data (FairRoad), which aims
to improve the fairness performances of recommender systems through the
construction of a small and carefully crafted antidote dataset. Toward this
end, we formulate our antidote data generation task as a mathematical
optimization problem, which minimizes the unfairness of the targeted
recommender systems while not disrupting the deployed recommendation
algorithms. Extensive experiments show that our proposed antidote data
generation algorithm significantly improve the fairness of recommender systems
with a small amounts of antidote data.
