Unbiased learning-to-rank (ULTR) is a well-established framework for learning from user
clicks, which are often biased by the ranker collecting the data. While theoretically justified …

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

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 …

While Learning to Rank (LTR) models on top of transformers have been widely adopted to
achieve decent performance, it is still challenging to train the model with sufficient data as …

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 …

This paper describes the approach of the THUIR team at the WSDM Cup 2023 Pre-training
for Web Search task. This task requires the participant to rank the relevant documents for …

Unbiased learning to rank (ULTR) aims to mitigate various biases existing in user clicks,
such as position bias, trust bias, presentation bias, and learn an effective ranker. In this …

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 …

We present CWRCzech, Click Web Ranking dataset for Czech, a 100M query-document
Czech click dataset for relevance ranking with user behavior data collected from search …

Eliminating examination bias accurately is pivotal to apply click-through data to train an
unbiased ranking model. However, most examination-bias estimators are limited to the …