In the past few years, there has been much work on incorporating fairness requirements into
algorithmic rankers, with contributions coming from the data management, algorithms …

We increasingly depend on a variety of data-driven algorithmic systems to assist us in many
aspects of life. Search engines and recommender systems among others are used as …

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 …

We present a framework for quantifying and mitigating algorithmic bias in mechanisms
designed for ranking individuals, typically used as part of web-scale search and …

Rankings are ubiquitous in the online world today. As we have transitioned from finding
books in libraries to ranking products, jobs, job applicants, opinions and potential romantic …

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 …

Abstract Conventional Learning-to-Rank (LTR) methods optimize the utility of the rankings to
the users, but they are oblivious to their impact on the ranked items. However, there has …

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

In the past few years, there has been much work on incorporating fairness requirements into
algorithmic rankers, with contributions coming from the data management, algorithms …

Data-driven decision making related to individuals has become increasingly pervasive, but
the issue concerning the potential discrimination has been raised by recent studies. In …