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

Recently there has been a growing interest in fairness-aware recommender
systems including fairness in providing consistent performance across different
users or groups of users. A recommender system could be considered unfair if
the recommendations do not fairly represent the tastes of a certain group of
users while other groups receive recommendations that are consistent with their
preferences. In this paper, we use a metric called miscalibration for measuring
how a recommendation algorithm is responsive to users' true preferences and we
consider how various algorithms may result in different degrees of
miscalibration for different users. In particular, we conjecture that
popularity bias which is a well-known phenomenon in recommendation is one
important factor leading to miscalibration in recommendation. Our experimental
results using two real-world datasets show that there is a connection between
how different user groups are affected by algorithmic popularity bias and their
level of interest in popular items. Moreover, we show that the more a group is
affected by the algorithmic popularity bias, the more their recommendations are
miscalibrated.

本研究考虑不同推荐算法对不同用户偏好的响应度进行测量，并揭示了算法中流行度偏差可能是导致推荐偏差的重要因素。两个真实数据集的实验结果表明，用户群体受到算法中流行度偏差程度与他们对流行物品的兴趣程度之间存在关联，并且受到算法中流行度偏差影响越大，他们的推荐结果就越有偏差。

The Connection Between Popularity Bias, Calibration, and Fairness in  Recommendation

Recently there has been a growing interest in fairness-aware recommender
systems, including fairness in providing consistent performance across
different users or groups of users. A recommender system could be considered
unfair if the recommendations do not fairly represent the tastes of a certain
group of users while other groups receive recommendations that are consistent
with their preferences. In this paper, we use a metric called miscalibration
for measuring how a recommendation algorithm is responsive to users' true
preferences and we consider how various algorithms may result in different
degrees of miscalibration. A well-known type of bias in recommendation is
popularity bias where few popular items are over-represented in
recommendations, while the majority of other items do not get significant
exposure. We conjecture that popularity bias is one important factor leading to
miscalibration in recommendation. Our experimental results using two real-world
datasets show that there is a strong correlation between how different user
groups are affected by algorithmic popularity bias and their level of interest
in popular items. Moreover, we show algorithms with greater popularity bias
amplification tend to have greater miscalibration.

本文介绍了一种度量推荐算法响应用户真实偏好的度量标准，称为 “miscalibration”。研究人员考虑了各种算法可能导致不同程度的 miscalibration，从而探讨了当算法流行度偏差导致 miscalibration 时的关键因素，其中研究者使用两种真实世界数据集展示了不同算法对用户群体的影响和流行偏差对 miscalibration 的影响。