# Multi-Feature Integration for Perception-Dependent Examination-Bias Estimation

arXiv: https://arxiv.org/abs/2302.13756 · HTML: https://mlsanity.com/paper/2302.13756/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2023.

## Built on by (most cited first, 5 of 5)

- [Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset](https://mlsanity.com/paper/2404.02543/) (2024)
- [Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank](https://mlsanity.com/paper/2309.15560/) (2023)
- [Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank](https://mlsanity.com/paper/2506.20501/) (2025)
- [Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rank](https://mlsanity.com/paper/2408.09817/) (2024)
- [CLAX: Fast and Flexible Neural Click Models in JAX](https://mlsanity.com/paper/2511.03620/) (2025)

## Abstract (arXiv)

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 hrefhttps://github.com/lixsh6/Tencentwsdmcup2023/tree/main/pytorchunbiashttps://github.com/lixsh6/Tencentwsdmcup2023