Recommender systems are typically designed to fulfill end user needs. However, in some domains the users are not the only stakeholders in the system. For instance, in a news aggregator website users, authors, magazines as well as the platform itself are potential stakeholders. Most of the collaborative filtering recommender systems suffer from popularity bias. Therefore, if the recommender system only considers users’ preferences, presumably it over-represents popular providers and under-represents less popular providers. To address this issue one should consider other stakeholders in the generated ranked lists. In this paper we demonstrate that hypergraph learning has the natural capability of handling a multi-stakeholder recommendation task. A hypergraph can model high order relations between different types of objects and therefore is naturally inclined to generate recommendation lists considering multiple stakeholders. We form the recommendations in time-wise rounds and learn to adapt the weights of stakeholders to increase the coverage of low-covered stakeholders over time. The results show that the proposed approach counters popularity bias and produces fairer recommendations with respect to authors in two news datasets, at a low cost in accuracy.

推荐系统通常旨在满足最终用户的需求。然而，在某些领域中，用户并不是系统中唯一的利益相关者。例如，在新闻聚合网站中，用户、作者、杂志以及平台本身都是潜在的利益相关者。大多数协同过滤推荐系统都存在流行偏见。因此，如果推荐系统只考虑用户的偏好，大概它代表了受欢迎的供应商，而代表了不太受欢迎的供应商。为了解决这个问题，应该考虑生成的排名列表中的其他利益相关者。在本文中，我们证明了超图学习具有自然的处理多利益相关者推荐任务的能力。超图可以模拟不同类型对象之间的高阶关系，因此自然倾向于生成考虑多个利益相关者的推荐列表。我们按时间轮次形成建议，并学习调整利益相关者的权重，以随着时间的推移增加低覆盖利益相关者的覆盖范围。结果表明，所提出的方法可以抵消流行偏见，并以较低的准确度成本针对两个新闻数据集中的作者产生更公平的推荐。