Abstract Recommender Systems (RSs) have assumed a crucial role in several digital
companies by directly affecting their key performance indicators. Nowadays, in this era of big …

Classical collaborative filtering, and content-based filtering methods try to learn a static
recommendation model given training data. These approaches are far from ideal in highly …

Click-through rate (CTR) prediction has been one of the most central problems in
computational advertising. Lately, embedding techniques that produce low-dimensional …

We introduce a novel algorithmic approach to content recommendation based on adaptive
clustering of exploration-exploitation (“bandit") strategies. We provide a sharp regret …

The main goal of this paper is to introduce the reader to the multiarmed bandit algorithms of
different types and to observe how the industry leveraged them in advancing …

We present a provably optimal differentially private algorithm for the stochastic multi-arm
bandit problem, as opposed to the private analogue of the UCB-algorithm (Mishra and …

How can we effectively recommend items to a user about whom we have no information?
This is the problem we focus on in this paper, known as the cold-start problem. In most …

Team structures---roles, norms, and interaction patterns---define how teams work. HCI
researchers have theorized ideal team structures and built systems nudging teams towards …

This work presents an extension of Thompson Sampling bandit policy for orchestrating the
collection of base recommendation algorithms for e-commerce. We focus on the problem of …

We study private and robust multi-armed bandits (MABs), where the agent receives Huber's
contaminated heavy-tailed rewards and meanwhile needs to ensure differential privacy. We …