Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

Rankings are ubiquitous in the online world today. As we have transitioned from finding
books in libraries to ranking products, jobs, job applicants, opinions and potential romantic …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

Rankings are the primary interface through which many online platforms match users to
items (eg news, products, music, video). In these two-sided markets, not only the users draw …

Ranking algorithms are deployed widely to order a set of items in applications such as
search engines, news feeds, and recommendation systems. Recent studies, however, have …

We introduce the concept of expected exposure as the average attention ranked items
receive from users over repeated samples of the same query. Furthermore, we advocate for …

The multi-armed bandit is an important framework for balancing exploration with exploitation
in recommendation. Exploitation recommends content (eg, products, movies, music playlists) …

Abstract Conventional Learning-to-Rank (LTR) methods optimize the utility of the rankings to
the users, but they are oblivious to their impact on the ranked items. However, there has …

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that
arise in quantum physics and random matrix theory. In contrast to traditional structured …

Recommender Systems are nowadays successfully used by all major web sites—from e-
commerce to social media—to filter content and make suggestions in a personalized way …