While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …

Recent advancements in machine learning and deep learning have brought algorithmic
fairness into sharp focus, illuminating concerns over discriminatory decision making that …

A well-known challenge in leveraging implicit user feedback like clicks to improve real-world
search services and recommender systems is its inherent bias. Most existing click models …

The logs of the use of a search engine provide sufficient data to train a better ranker.
However, it is well known that such implicit feedback reflects biases, and in particular a …

Clicks on rankings suffer from position bias: generally items on lower ranks are less likely to
be examined—and thus clicked—by users, in spite of their actual preferences between …

Unbiased recommender learning has been actively studied to alleviate the inherent bias of
implicit datasets under the missing-not-at-random assumption. Existing studies solely …

Click-based learning to rank (LTR) tackles the mismatch between click frequencies on items
and their actual relevance. The approach of previous work has been to assume a model of …

Unbiased learning to rank (ULTR) studies the problem of mitigating various biases from
implicit user feedback data such as clicks, and has been receiving considerable attention …

Abstract Off-policy Learning to Rank (LTR) aims to optimize a ranker from data collected by a
deployed logging policy. However, existing off-policy learning to rank methods often make …

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 …