People are susceptible to an array of cognitive biases, which can result in systematic errors
and deviations from rational decision making. Over the past decade, an increasing amount …

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

A Conversational Recommender System (CRS) offers increased transparency and control to
users by enabling them to engage with the system through a real-time multi-turn dialogue …

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

Implicit feedback (eg, clicks, dwell times, etc.) is an abundant source of data in human-
interactive systems. While implicit feedback has many advantages (eg, it is inexpensive to …

Recommendation is a prevalent and critical service in information systems. To provide
personalized suggestions to users, industry players embrace machine learning, more …

A well-known challenge in learning from click data is its inherent bias and most notably
position bias. Traditional click models aim to extract the‹ query, document› relevance and …

Online information intermediaries such as Facebook and Google are slowly replacing
traditional media channels thereby partly becoming the gatekeepers of our society. To deal …

Learning to rank with biased click data is a well-known challenge. A variety of methods has
been explored to debias click data for learning to rank such as click models, result …

Click-through data has proven to be a critical resource for improving search ranking quality.
Though a large amount of click data can be easily collected by search engines, various …