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。