Interplay between Upsampling and Regularization for Provider Fairness in Recommender Systems
This work addresses fairness for item providers in multi-sided recommender systems, but it is incremental as it builds on prior methods to handle specific scenarios like multiple providers per item.
The study tackled provider fairness in recommender systems by addressing biases in relevance scores and disparities in visibility and exposure for minority provider groups, and demonstrated that a combined treatment of upsampling and regularization reduces disparate relevance and improves fairness with negligible utility loss.
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.