An increasing number of decisions regarding the daily lives of human beings are being
controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging …

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 …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Conversational information seeking (CIS) is concerned with a sequence of interactions
between one or more users and an information system. Interactions in CIS are primarily …

Recommendation algorithms are known to suffer from popularity bias; a few popular items
are recommended frequently while the majority of other items are ignored. These …

Recommender systems are nowadays a pervasive part of our online user experience, where
they either serve as information filters or provide us with suggestions for additionally relevant …

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

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 …

Many recommender systems suffer from popularity bias: popular items are recommended
frequently while less popular, niche products, are recommended rarely or not at all …

Rankings are the primary interface through which many online platforms match users to
items (eg news, products, music, video). In these two-sided markets, not only the users draw …