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 …

Search, recommendation, and online advertising are the three most important information-
providing mechanisms on the web. These information seeking techniques, satisfying users' …

Abstract Meta-, multi-task, and federated learning can be all viewed as solving similar tasks,
drawn from a distribution that reflects task similarities. We provide a unified view of all these …

Most recommender systems recommend a list of items. The user examines the list, from the
first item to the last, and often chooses the first attractive item and does not examine the rest …

Media services providers, such as music streaming platforms, frequently leverage swipeable
carousels to recommend personalized content to their users. However, selecting the most …

Traditional recommender systems rely on user feedback such as ratings or clicks to the
items, to analyze the user interest and provide personalized recommendations. However …

How to obtain an unbiased ranking model by learning to rank with biased user feedback is
an important research question for IR. Existing work on unbiased learning to rank (ULTR) …

Online learning to rank is a core problem in information retrieval and machine learning.
Many provably efficient algorithms have been recently proposed for this problem in specific …

Sequentially learning to place items in multi-position displays or lists is a task that can be
cast into the multiple-play semi-bandit setting. However, a major concern in this context is …

Motivated by recommendation problems in music streaming platforms, we propose a
nonstationary stochastic bandit model in which the expected reward of an arm depends on …