Recommender systems can strongly influence which information we see online, eg, on
social media, and thus impact our beliefs, decisions, and actions. At the same time, these …

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 …

In recent years, the problem of addressing fairness in machine learning (ML) and automatic
decision making has attracted a lot of attention in the scientific communities dealing with …

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 …

In order to support the study of recent advances in recommender systems, this paper
presents an extended recommendation library consisting of eight packages for up-to-date …

Research in machine learning fairness has historically considered a single binary
demographic attribute; however, the reality is of course far more complicated. In this work …

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

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 …

Clustering algorithms are a class of unsupervised machine learning (ML) algorithms that
feature ubiquitously in modern data science, and play a key role in many learning-based …

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 …