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

Multi-interest collaborative metric learning has recently emerged as an effective approach to
modeling the multifaceted interests of a user in recommender systems. However, two issues …

Abstract Collaborative Metric Learning (CML) has recently emerged as a popular method in
recommendation systems (RS), closing the gap between metric learning and Collaborative …

A common explanation for negative user impacts of content recommender systems is
misalignment between the platform's objective and user welfare. In this work, we show that …

Optimizing user engagement is a key goal for modern recommendation systems, but blindly
pushing users towards increased consumption risks burn-out, churn, or even addictive …

Modern recommender systems lie at the heart of complex recommender ecosystems that
couple the behavior of users, content providers, vendors, advertisers, and other actors …

When making recommendations, there is an apparent trade-off between the goals of
accuracy (to recommend items a user is most likely to want) and diversity (to recommend …