  Personalized recommendation brings about novel challenges in ensuring
fairness, especially in scenarios in which users are not the only stakeholders
involved in the recommender system. For example, the system may want to ensure
that items from different providers have a fair chance of being recommended. To
solve this problem, we propose a Fairness-Aware Re-ranking algorithm (FAR) to
balance the ranking quality and provider-side fairness. We iteratively generate
the ranking list by trading off between accuracy and the coverage of the
providers. Although fair treatment of providers is desirable, users may differ
in their receptivity to the addition of this type of diversity. Therefore,
personalized user tolerance towards provider diversification is incorporated.
Experiments are conducted on both synthetic and real-world data. The results
show that our proposed re-ranking algorithm can significantly promote fairness
with a slight sacrifice in accuracy and can do so while being attentive to
individual user differences.
