  Considering the impact of recommendations on item providers is one of the
duties of multi-sided recommender systems. Item providers are key stakeholders
in online platforms, and their earnings and plans are influenced by the
exposure their items receive in recommended lists. Prior work showed that
certain minority groups of providers, characterized by a common sensitive
attribute (e.g., gender or race), are being disproportionately affected by
indirect and unintentional discrimination. Our study in this paper handles a
situation where ($i$) the same provider is associated with multiple items of a
list suggested to a user, ($ii$) an item is created by more than one provider
jointly, and ($iii$) predicted user-item relevance scores are biasedly
estimated for items of provider groups. Under this scenario, we assess
disparities in relevance, visibility, and exposure, by simulating diverse
representations of the minority group in the catalog and the interactions.
Based on emerged unfair outcomes, we devise a treatment that combines
observation upsampling and loss regularization, while learning user-item
relevance scores. Experiments on real-world data demonstrate that our treatment
leads to lower disparate relevance. The resulting recommended lists show fairer
visibility and exposure, higher minority item coverage, and negligible loss in
recommendation utility.
