Fairness-aware recommender systems that have a provider-side fairness concern
seek to ensure that protected group(s) of providers have a fair opportunity to
promote their items or products. There is a ``cost of fairness'' borne by the
consumer side of the interaction when such a solution is implemented. This
consumer-side cost raises its own questions of fairness, particularly when
personalization is used to control the impact of the fairness constraint. In
adopting a personalized approach to the fairness objective, researchers may be
opening their systems up to strategic behavior on the part of users. This type
of incentive has been studied in the computational social choice literature
under the terminology of ``bossiness''. The concern is that a bossy user may be
able to shift the cost of fairness to others, improving their own outcomes and
worsening those for others. This position paper introduces the concept of
bossiness, shows its application in fairness-aware recommendation and discusses
strategies for reducing this strategic incentive.

介绍了针对提供者公平性问题的公平感知推荐系统，分析了在使用个性化方法以实现公平目标时存在的挑战，特别是 bossiness 问题，并提供了减少这一策略性刺激的策略。

谁来付费？个性化、专横和公平的成本

Who Pays? Personalization, Bossiness and the Cost of Fairness

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.

本篇研究提出了一种考虑到用户、排名质量和供应商公平性的新型推荐算法 FRA，在实验中，该算法在牺牲一定精度的情况下，有效提升了推荐系统的公平性，并考虑到了个体用户的偏好。