Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank. (arXiv:2206.01702v1 [cs.IR])

Unbiased learning to rank (ULTR) aims to train an unbiased ranking model from
biased user click logs. Most of the current ULTR methods are based on the
examination hypothesis (EH), which assumes that the click probability can be
factorized into two scalar functions, one related to ranking features and the
other related to bias factors. Unfortunately, the interactions among features,
bias factors and clicks are complicated in practice, and usually cannot be
factorized in this independent way. Fitting click data …

arxiv learning unbiased vectorization