The service system involves multiple stakeholders, making it crucial to ensure fairness. In
this paper, we take the example of a typical service system, the recommender system, to …

Recommender systems mitigate information overload by offering personalized suggestions
to users. As the interactions between users and items can inherently be depicted as a …

Recommender systems are typically biased toward a small group of users, leading to severe
unfairness in recommendation performance, ie, User-Oriented Fairness (UOF) issue …

Federated Learning (FL) employs a training approach to address scenarios where users'
data cannot be shared across clients. Achieving fairness in FL is imperative since training …

The interaction data used for training recommender systems often exhibit a long-tail
distribution. Such highly imbalanced data distribution results in an unfair learning process …

Recommender systems often manifest biases toward a small user group, resulting in
pronounced disparities in recommendation performance, ie, the User-Oriented Fairness …