Recommendation algorithms are widely used nowadays, especially in scenarios of
information overload (ie, when users have too many options to choose from), due to their …

Popularity bias is defined as the intrinsic tendency of recommendation algorithms to feature
popular items more than unpopular ones in the ranked lists lists they produced. When …

When designing data science (DS) pipelines, end-users can get overwhelmed by the large
and growing set of available data preprocessing and modeling techniques. Intelligent …

More and more aspects of our everyday lives are influenced by automated decisions made
by systems that statistically analyze traces of our activities. It is thus natural to question …

Digital platforms, such as social media and e-commerce websites, widely make use of
Recommender Systems to provide value to users. However, social consequences of such …

Recommendation Systems (RS) are often plagued by popularity bias. Specifically, when
recommendation models are trained on long-tailed datasets, they not only inherit this bias …

Information Visualization has been one of the cornerstones of Data Science, turning the
abundance of Big Data being produced through modern systems into actionable knowledge …

Modern online platforms offer users an opportunity to participate in a variety of content-
creation, social networking, and shopping activities. With the rapid proliferation of such …

Throughout our digital lives, we are getting recommendations for about almost everything
we do, buy or consume. However, it is often the case that recommenders cannot locate the …

A growing number of applications enable users to form groups for activities, like visiting a
restaurant or watching a movie, making group recommenders more prevalent than ever …