Recommender systems are typically biased toward a small group of users,
leading to severe unfairness in recommendation performance, i.e., User-Oriented
Fairness (UOF) issue. The existing research on UOF is limited and fails to deal
with the root cause of the UOF issue: the learning process between advantaged
and disadvantaged users is unfair. To tackle this issue, we propose an
In-processing User Constrained Dominant Sets (In-UCDS) framework, which is a
general framework that can be applied to any backbone recommendation model to
achieve user-oriented fairness. We split In-UCDS into two stages, i.e., the
UCDS modeling stage and the in-processing training stage. In the UCDS modeling
stage, for each disadvantaged user, we extract a constrained dominant set (a
user cluster) containing some advantaged users that are similar to it. In the
in-processing training stage, we move the representations of disadvantaged
users closer to their corresponding cluster by calculating a fairness loss. By
combining the fairness loss with the original backbone model loss, we address
the UOF issue and maintain the overall recommendation performance
simultaneously. Comprehensive experiments on three real-world datasets
demonstrate that In-UCDS outperforms the state-of-the-art methods, leading to a
fairer model with better overall recommendation performance.

推荐系统中存在用户取向公平性（UOF）问题，为了解决这个问题，我们提出了一个通用的 In-UCDS 框架，该框架可以应用于任何骨干推荐模型以实现用户取向公平，并且通过联合公平损失和原始骨干模型损失来维护整体推荐性能。

在推荐系统中处理用户约束的主导集以实现用户导向的公平性

In-processing User Constrained Dominant Sets for User-Oriented Fairness  in Recommender Systems

Recent work in recommender systems mainly focuses on fairness in
recommendations as an important aspect of measuring recommendations quality. A
fairness-aware recommender system aims to treat different user groups
similarly. Relevant work on user-oriented fairness highlights the
discriminative behavior of fairness-unaware recommendation algorithms towards a
certain user group, defined based on users' activity level. Typical solutions
include proposing a user-centered fairness re-ranking framework applied on top
of a base ranking model to mitigate its unfair behavior towards a certain user
group i.e., disadvantaged group. In this paper, we re-produce a user-oriented
fairness study and provide extensive experiments to analyze the dependency of
their proposed method on various fairness and recommendation aspects, including
the recommendation domain, nature of the base ranking model, and user grouping
method. Moreover, we evaluate the final recommendations provided by the
re-ranking framework from both user- (e.g., NDCG, user-fairness) and item-side
(e.g., novelty, item-fairness) metrics. We discover interesting trends and
trade-offs between the model's performance in terms of different evaluation
metrics. For instance, we see that the definition of the
advantaged/disadvantaged user groups plays a crucial role in the effectiveness
of the fairness algorithm and how it improves the performance of specific base
ranking models. Finally, we highlight some important open challenges and future
directions in this field. We release the data, evaluation pipeline, and the
trained models publicly on this https URL.

研究在推荐系统中的公平性问题，提出一种基于用户为导向的公平性重排框架来缓解基于排名的不公平行为，发现不同用户组的定义在算法的有效性和特定排名模型的性能改进方面起到至关重要的作用。