Unbiased Learning to Rank (ULTR), ie, learning to rank documents with biased user
feedback data, is a well-known challenge in information retrieval. Existing methods in …

Recent advances in unbiased learning to rank (LTR) count on Inverse Propensity Scoring
(IPS) to eliminate bias in implicit feedback. Though theoretically sound in correcting the bias …

The goal of unbiased learning to rank (ULTR) is to leverage implicit user feedback for
optimizing learning-to-rank systems. Among existing solutions, automatic ULTR algorithms …

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 …

The evaluation of recommender systems relies on user preference data, which is difficult to
acquire directly because of its subjective nature. Current recommender systems widely …

Many applications of RCTs involve the presence of multiple treatment administrators--from
field experiments to online advertising--that compete for the subjects' attention. In the face of …

In video recommendation, an ongoing effort is to satisfy users' personalized information
needs by leveraging their logged watch time. However, watch time prediction suffers from …

Modern Information Retrieval (IR) systems, such as search engines, recommender systems,
and conversational agents, are best thought of as interactive systems. And their …