Recommender systems are software applications that help users to find items of interest in
situations of information overload. Current research often assumes a one-shot interaction …

Recommender systems are one of the most successful applications of data mining and
machine-learning technology in practice. Academic research in the field is historically often …

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 …

Social media platforms moderate content for each user by incorporating the outputs of both
platform-wide content moderation systems and, in some cases, user-configured personal …

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 …

Recommender systems usually face the issue of filter bubbles: over-recommending
homogeneous items based on user features and historical interactions. Filter bubbles will …

Online symptom checkers (OSC) are widely used intelligent systems in health contexts such
as primary care, remote healthcare, and epidemic control. OSCs use algorithms such as …

Reading or viewing recommendations are a common feature on modern media sites. What
is shown to consumers as recommendations is nowadays often automatically determined by …

As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

Recommender systems (RS) are on the rise in many domains. While they offer great
promises, they also raise concerns: lack of transparency, reduction of diversity, little to no …