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 …

Social data in digital form—including user-generated content, expressed or implicit relations
between people, and behavioral traces—are at the core of popular applications and …

Algorithms are ubiquitous and critical sources of information online, increasingly acting as
gatekeepers for users accessing or sharing information about virtually any topic, including …

An introduction to the intellectual foundations and practical utility of the recent work on
fairness and machine learning. Fairness and Machine Learning introduces advanced …

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

Rankings of people and items are at the heart of selection-making, match-making, and
recommender systems, ranging from employment sites to sharing economy platforms. As …

Two-sided marketplaces are platforms that have customers not only on the demand side (eg
users), but also on the supply side (eg retailer, artists). While traditional recommender …

We introduce the concept of expected exposure as the average attention ranked items
receive from users over repeated samples of the same query. Furthermore, we advocate for …

In the research literature, evaluations of recommender system effectiveness typically report
results over a given data set, providing an aggregate measure of effectiveness over each …

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 …