# Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rank

arXiv: https://arxiv.org/abs/2408.09817 · HTML: https://mlsanity.com/paper/2408.09817/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [A Decomposable Attention Model for Natural Language Inference](https://mlsanity.com/paper/1606.01933/) (2016)
- [Unbiased Learning-to-Rank with Biased Feedback](https://mlsanity.com/paper/1608.04468/) (2016)
- [Unbiased Learning to Rank with Unbiased Propensity Estimation](https://mlsanity.com/paper/1804.05938/) (2018)
- [Estimating Position Bias without Intrusive Interventions](https://mlsanity.com/paper/1812.05161/) (2018)
- [A Large Scale Search Dataset for Unbiased Learning to Rank](https://mlsanity.com/paper/2207.03051/) (2022)
- [THUIR at WSDM Cup 2023 Task 1: Unbiased Learning to Rank](https://mlsanity.com/paper/2304.12650/) (2023)
- [Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset](https://mlsanity.com/paper/2404.02543/) (2024)
- [ULTRA: An Unbiased Learning To Rank Algorithm Toolbox](https://mlsanity.com/paper/2108.05073/) (2021)
- [Multi-Feature Integration for Perception-Dependent Examination-Bias Estimation](https://mlsanity.com/paper/2302.13756/) (2023)
- [Feature-Enhanced Network with Hybrid Debiasing Strategies for Unbiased Learning to Rank](https://mlsanity.com/paper/2302.07530/) (2023)
- [CIR at the NTCIR-17 ULTRE-2 Task](https://mlsanity.com/paper/2310.11852/) (2023)

## Abstract (arXiv)

Unbiased Learning to Rank (ULTR) aims to leverage biased implicit user feedback (e.g., click) to optimize an unbiased ranking model. The effectiveness of the existing ULTR methods has primarily been validated on synthetic datasets. However, their performance on real-world click data remains unclear. Recently, Baidu released a large publicly available dataset of their web search logs. Subsequently, the NTCIR-17 ULTRE-2 task released a subset dataset extracted from it. We conduct experiments on commonly used or effective ULTR methods on this subset to determine whether they maintain their effectiveness. In this paper, we propose a Contextual Dual Learning Algorithm with Listwise Distillation (CDLA-LD) to simultaneously address both position bias and contextual bias. We utilize a listwise-input ranking model to obtain reconstructed feature vectors incorporating local contextual information and employ the Dual Learning Algorithm (DLA) method to jointly train this ranking model and a propensity model to address position bias. As this ranking model learns the interaction information within the documents list of the training set, to enhance the ranking model's generalization ability, we additionally train a pointwise-input ranking model to learn the listwise-input ranking model's capability for relevance judgment in a listwise manner. Extensive experiments and analysis confirm the effectiveness of our approach.