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

The original filter bubble thesis states that the use of personalization algorithms results in a
unique universe of information for each of us, with far-reaching individual and societal …

Personalized recommendation benefits users in accessing contents of interests effectively.
Current research on recommender systems mostly focuses on matching users with proper …

Research to date aimed at the fairness, accountability, and transparency of algorithmic
systems has largely focused on topics such as identifying failures of current systems and on …

Recommender systems'(RSs) research has mostly focused on algorithms aimed at
improving platform owners' revenues and user's satisfaction. However, RSs have additional …

With the emergence of various information access systems exhibiting increasing complexity,
there is a critical need for sound and scalable means of automatic evaluation. To address …

The content that a recommender system (RS) shows to users influences them. Therefore,
when choosing a recommender to deploy, one is implicitly also choosing to induce specific …

Mitigating bias in algorithmic systems is a critical issue drawing attention across
communities within the information and computer sciences. Given the complexity of the …

Automated recommendations can nowadays be found on many e-commerce platforms, and
such recommendations can create substantial value for consumers and providers. Often …

In an effort to regulate Machine Learning-driven (ML) systems, current auditing processes
mostly focus on detecting harmful algorithmic biases. While these strategies have proven to …