Two-sided marketplaces are platforms that have customers not only on the demand side (eg
users), but also on the supply side (eg retailer, artists). While traditional recommender …

Learning policies on data synthesized by models can in principle quench the thirst of
reinforcement learning algorithms for large amounts of real experience, which is often costly …

Users of music streaming, video streaming, news recommendation, and e-commerce
services often engage with content in a sequential manner. Providing and evaluating good …

Bandit algorithms have gained increased attention in recommender systems, as they
provide effective and scalable recommendations. These algorithms use reward functions …

One of the main problems of web recommender systems is exposure bias, due to the fact
that the web system itself is partly generating its own future, as users can only click on items …

Offline evaluation has a widespread use in the development of recommender systems. In
order to perform offline evaluation, an Information Retrieval practitioner has to make several …

Evolutionary Strategies (ES) are a popular family of black-box zeroth-order optimization
algorithms which rely on search distributions to efficiently optimize a large variety of …

One of the questions that arises when designing models that learn to solve multiple tasks
simultaneously is how much of the available training budget should be devoted to each …

Because logged data has become ubiquitous in wide-range applications and since
onlineexploration may be sensitive, counterfactual methods have gained significant …

Counterfactual reasoning from logged data has become increasingly important for many
applications such as web advertising or healthcare. In this paper, we address the problem of …