Fairness is a critical system-level objective in recommender systems that has
been the subject of extensive recent research. A specific form of fairness is
supplier exposure fairness where the objective is to ensure equitable coverage
of items across all suppliers in recommendations provided to users. This is
especially important in multistakeholder recommendation scenarios where it may
be important to optimize utilities not just for the end-user, but also for
other stakeholders such as item sellers or producers who desire a fair
representation of their items. This type of supplier fairness is sometimes
accomplished by attempting to increasing aggregate diversity in order to
mitigate popularity bias and to improve the coverage of long-tail items in
recommendations. In this paper, we introduce FairMatch, a general graph-based
algorithm that works as a post processing approach after recommendation
generation to improve exposure fairness for items and suppliers. The algorithm
iteratively adds high quality items that have low visibility or items from
suppliers with low exposure to the users' final recommendation lists. A
comprehensive set of experiments on two datasets and comparison with
state-of-the-art baselines show that FairMatch, while significantly improves
exposure fairness and aggregate diversity, maintains an acceptable level of
relevance of the recommendations.

本文介绍了一种基于图形算法的后处理方法 FairMatch，可在推荐生成后改善物品和供应商的曝光公平性，实现多方利益相关方的公平效益最大化。在两个数据集上的一系列实验和与最新基线模型的比较表明，FairMatch 显著提高了曝光公平性和聚合多样性，同时保持了推荐的相关性水平。

A Graph-based Approach for Mitigating Multi-sided Exposure Bias in  Recommender Systems

Recommender systems are often biased toward popular items. In other words,
few items are frequently recommended while the majority of items do not get
proportionate attention. That leads to low coverage of items in recommendation
lists across users (i.e. low aggregate diversity) and unfair distribution of
recommended items. In this paper, we introduce FairMatch, a general graph-based
algorithm that works as a post-processing approach after recommendation
generation for improving aggregate diversity. The algorithm iteratively finds
items that are rarely recommended yet are high-quality and add them to the
users' final recommendation lists. This is done by solving the maximum flow
problem on the recommendation bipartite graph. While we focus on aggregate
diversity and fair distribution of recommended items, the algorithm can be
adapted to other recommendation scenarios using different underlying
definitions of fairness. A comprehensive set of experiments on two datasets and
comparison with state-of-the-art baselines show that FairMatch, while
significantly improving aggregate diversity, provides comparable recommendation
accuracy.

介绍一种针对推荐系统中存在的偏见的一般性的基于图的算法 FairMatch，可在推荐生成后作为后处理方法来提高推荐的多样性和公平性，通过在推荐二分图上解决最大流问题来找到很少推荐但却是高质量的项目并将其添加到用户的最终推荐列表中。