Ranking, recommendation, and retrieval systems are widely used in online platforms and
other societal systems, including e-commerce, media-streaming, admissions, gig platforms …

As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

Recommender systems are an essential tool to relieve the information overload challenge
and play an important role in people's daily lives. Since recommendations involve …

Recently, there has been a rising awareness that when machine learning (ML) algorithms
are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or …

This paper explores the biases inherent in ChatGPT-based recommender systems, focusing
on provider fairness (item-side fairness). Through extensive experiments and over a …

In this paper, we address the issue of recommending fairly from the aspect of providers,
which has become increasingly essential in multistakeholder recommender systems …

Explicit finite-sample statistical guarantees on model performance are an important
ingredient in responsible machine learning. Previous work has focused mainly on bounding …

There is growing interest in designing recommender systems that aim at being fair towards
item producers or their least satisfied users. Inspired by the domain of inequality …

The prevalence and importance of algorithmic two-sided marketplaces has drawn attention
to the issue of fairness in such settings. Algorithmic decisions are used in assigning students …

Fair re-ranking aims to redistribute ranking slots among items more equitably to ensure
responsibility and ethics. The exploration of redistribution problems has a long history in …