The comprehensive evaluation of the performance of a recommender system is a complex
endeavor: many facets need to be considered in configuring an adequate and effective …

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

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 …

While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …

Recommendation, information retrieval, and other information access systems pose unique
challenges for investigating and applying the fairness and non-discrimination concepts that …

As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision making. The satisfaction of users and …

Prior research on exposure fairness in the context of recommender systems has focused
mostly on disparities in the exposure of individual or groups of items to individual users of …

Recommender systems are the algorithms which select, filter, and personalize content
across many of the world's largest platforms and apps. As such, their positive and negative …

Fairness is fundamental to all information access systems, including recommender systems.
However, the landscape of fairness definition and measurement is quite scattered with many …

Information access systems, such as search and recommender systems, often use ranked
lists to present results believed to be relevant to the user's information need. Evaluating …