  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 hypothesis of Position-Based Model (PBM), which
supposes that the calculation of examination bias only depends on the rank of
the document. Recently, although some works introduce information such as
clicks in the same query list and contextual information when calculating the
examination bias, they still do not model the impact of document representation
on search engine result pages (SERPs) that seriously affects one's perception
of document relevance to a query when examining. Therefore, we propose a
Multi-Feature Integration Model (MFIM) where the examination bias depends on
the representation of document except the rank of it. Furthermore, we mine a
key factor slipoff counts that can indirectly reflects the influence of all
perception-bias factors. Real world experiments on Baidu-ULTR dataset
demonstrate the superior effectiveness and robustness of the new approach. The
source code is available at
\href{https://github.com/lixsh6/Tencent_wsdm_cup2023/tree/main/pytorch_unbias}{https://github.com/lixsh6/Tencent\_wsdm\_cup2023}
