Online platforms mediate access to opportunity: relevance-based rankings create and
constrain options by allocating exposure to job openings and job candidates in hiring …

We tackle, in the multiple-play bandit setting, the online ranking problem of assigning L
items to K predefined positions on a web page in order to maximize the number of user …

Abstract System-oriented IR evaluations are limited to rather abstract understandings of real
user behavior. As a solution, simulating user interactions provides a cost-efficient way to …

While the Cranfield evaluation methodology based on test collections has been very useful
for evaluating simple IR systems that return a ranked list of documents, it has significant …

Abstract Stochastic Rank-One Bandits are a simple framework for regret minimization
problems over rank-one matrices of arms. The initially proposed algorithms are proved to …

We propose a novel framework for structured bandits, which we call an influence diagram
bandit. Our framework uses a graphical model to capture complex statistical dependencies …

In counterfactual learning to rank (CLTR) user interactions are used as a source of
supervision. Since user interactions come with bias, an important focus of research in this …

Two-tower architecture is commonly used in real-world systems for Unbiased Learning to
Rank (ULTR), where a Deep Neural Network (DNN) tower models unbiased relevance …

Users' click-through behavior is considered as a valuable yet noisy source of implicit
relevance feedback for web search engines. A series of click models have therefore been …

Most of the current models that are used to simulate users in Interactive Information Retrieval
(IIR) lack realism and agency. Such models generally make decisions in a stochastic …