  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 precision.
