Since its inception, the field of unbiased learning to rank (ULTR) has remained very active
and has seen several impactful advancements in recent years. This tutorial provides both an …

Ranking systems are ubiquitous in modern Internet services, including online marketplaces,
social media, and search engines. Traditionally, ranking systems only focus on how to get …

Pre-trained language representation models (PLMs) such as BERT and Enhanced
Representation through kNowledge IntEgration (ERNIE) have been integral to achieving …

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 …

Extracting query-document relevance from the sparse, biased clickthrough log is among the
most fundamental tasks in the web search system. Prior art mainly learns a relevance …

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 …

Authors' addresses: Sanaz Keshvari, Ferdowsi University of Mashhad, Mashhad, Iran,;
Farzan Saeedi, Ferdowsi University of Mashhad, Mashhad, Iran,; Hadi Sadoghi Yazdi …

The Chinese academy of sciences Information Retrieval team (CIR) has participated in the
NTCIR-17 ULTRE-2 task. This paper describes our approaches and reports our results on …

Counterfactual learning to rank (CLTR) has attracted extensive attention in the IR community
for its ability to leverage massive logged user interaction data to train ranking models. While …

Our goal is a mechanism for efficiently assigning scalar ratings to each of a large set of
elements. For example," what percent positive or negative is this product review?" When …