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Unbiased Learning to Rank: Counterfactual and Online Approaches

Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke
2020 Companion Proceedings of the Web Conference 2020  
We will provide an overview of the two main families of methods in Unbiased Learning to Rank: Counterfactual Learning to Rank (CLTR) and Online Learning to Rank (OLTR) and their underlying theory.  ...  This tutorial is about Unbiased Learning to Rank, a recent research field that aims to learn unbiased user preferences from biased user interactions.  ...  We will provide an overview of the two main families of methods in Unbiased Learning to Rank: Counterfactual Learning to Rank (CLTR) and Online Learning to Rank (OLTR) and their underlying theory.  ... 
doi:10.1145/3366424.3383107 dblp:conf/www/OosterhuisJR20 fatcat:lbuptnrrvbapllvdegqygtlkqm

Unbiased Learning to Rank: Online or Offline? [article]

Qingyao Ai, Tao Yang, Huazheng Wang, Jiaxin Mao
2020 arXiv   pre-print
In this paper, we formalize the task of unbiased learning to rank and show that existing algorithms for offline unbiased learning and online learning to rank are just the two sides of the same coin.  ...  studies on unbiased parameters estimation with real-time user interactions, namely the online learning to rank.  ...  We refer to these approaches as the unbiased learning to rank (ULTR) approaches.  ... 
arXiv:2004.13574v3 fatcat:53qz55i47bdjdopxyqjvsiym6u

Unbiased Learning to Rank with Unbiased Propensity Estimation

Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft
2018 The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval - SIGIR '18  
It can adapt to the change of bias distributions and is applicable to online learning.  ...  A variety of methods has been explored to debias click data for learning to rank such as click models, result interleaving and, more recently, the unbiased learning-to-rank framework based on inverse propensity  ...  OUR APPROACH We now describe our approach for automatic unbiased learning to rank.  ... 
doi:10.1145/3209978.3209986 dblp:conf/sigir/AiBLGC18 fatcat:vogiohn3sren7mnun2wu5sprf4

ULTRA: An Unbiased Learning To Rank Algorithm Toolbox [article]

Anh Tran, Tao Yang, Qingyao Ai
2021 arXiv   pre-print
Existing studies on unbiased learning to rank (ULTR) can be generalized into two families-algorithms that attain unbiasedness with logged data, offline learning, and algorithms that achieve unbiasedness  ...  by estimating unbiased parameters with real-time user interactions, namely online learning.  ...  Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor.  ... 
arXiv:2108.05073v1 fatcat:3whuqzhjtnbxdfw4tghpoj7zle

Unbiased Learning-to-Rank with Biased Feedback

Thorsten Joachims, Adith Swaminathan, Tobias Schnabel
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
To overcome this bias problem, we present a counterfactual inference framework that provides the theoretical basis for unbiased LTR via Empirical Risk Minimization despite biased data.  ...  For example, position bias in search rankings strongly influences how many clicks a result receives, so that directly using click data as a training signal in Learning-to-Rank (LTR) methods yields sub-optimal  ...  We thank Maarten de Rijke, Alexey Borisov, Artem Grotov, and Yuning Mao for valuable feedback and discussions.  ... 
doi:10.24963/ijcai.2018/738 dblp:conf/ijcai/JoachimsSS18 fatcat:2xi5h6pw5rdrpjx7lcwmhrlzdy

Model-based Unbiased Learning to Rank [article]

Dan Luo, Lixin Zou, Qingyao Ai, Zhiyu Chen, Dawei Yin, Brian D. Davison
2022 arXiv   pre-print
Unbiased Learning to Rank~(ULTR) that learns to rank documents with biased user feedback data is a well-known challenge in information retrieval.  ...  To address this problem, we propose a model-based unbiased learning-to-rank framework.  ...  LEARNING TO RANK In this section, we more fully describe our model-based unbiased learning to rank approach.  ... 
arXiv:2207.11785v2 fatcat:lcfsb5ubnjgldfmnuyqataubju

Unbiased Learning-to-Rank with Biased Feedback

Thorsten Joachims, Adith Swaminathan, Tobias Schnabel
2017 Proceedings of the Tenth ACM International Conference on Web Search and Data Mining - WSDM '17  
To overcome this bias problem, we present a counterfactual inference framework that provides the theoretical basis for unbiased LTR via Empirical Risk Minimization despite biased data.  ...  In contrast to most conventional approaches to de-bias the data using click models, this allows training of ranking functions even in settings where queries do not repeat.  ...  We thank Maarten de Rijke, Alexey Borisov, Artem Grotov, and Yuning Mao for valuable feedback and discussions.  ... 
doi:10.1145/3018661.3018699 dblp:conf/wsdm/JoachimsSS17 fatcat:bvcuscpk2vbyro3rhboyhzo3di

Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to Rank [article]

Harrie Oosterhuis
2022 arXiv   pre-print
The prevalent approach to unbiased click-based learning-to-rank (LTR) is based on counterfactual inverse-propensity-scoring (IPS) estimation.  ...  In contrast with general reinforcement learning, counterfactual doubly-robust (DR) estimation has not been applied to click-based LTR in previous literature.  ...  All content represents the opinion of the authors, which is not necessarily shared or endorsed by their respective employers and/or sponsors.  ... 
arXiv:2203.17118v2 fatcat:6z74x6ukynestnqneb6hgge6xm

Unbiased Top-k Learning to Rank with Causal Likelihood Decomposition [article]

Haiyuan Zhao, Jun Xu, Xiao Zhang, Guohao Cai, Zhenhua Dong, Ji-Rong Wen
2022 arXiv   pre-print
Advantages of CLD include theoretical soundness and a unified framework for pointwise and pairwise unbiased top-k learning to rank.  ...  Existing unbiased learning to rank approaches either focus on one type of bias (e.g., position bias) or mitigate the position bias and sample selection bias with separate components, overlooking their  ...  to Rank (ULTR) [4, 5, 18] or Counterfactual Learning to Rank (CLTR) [1, 19, 29] .  ... 
arXiv:2204.00815v1 fatcat:vfdx4xdvtfet7cg2ujfrihtg7a

Unbiased Pairwise Approach toward Learning-to-Rank: An Empirical Study

Tran Manh Ha
2022 REV Journal on Electronics and Communications  
The aim of this study is to explore and experiment the existing learning-to-rank approaches with real-life logs data.  ...  Learning-to-rank comes out as one of the solutions to ease out the mentioned obstacle by trying to rearrange objects according to their degrees of importance or relevance.  ...  ACKNOWLEDGMENT We would like to thank the NAB Studio data team and the team leader, Mr. Hoang Gia Vu, for the company's collected dataset and continuous support.  ... 
doi:10.21553/rev-jec.281 fatcat:oq4zgdrcqnge7ptnh5katqchrm

Fair Learning-to-Rank from Implicit Feedback [article]

Himank Yadav, Zhengxiao Du, Thorsten Joachims
2019 arXiv   pre-print
In both cases, the learned ranking policy can be unfair and lead to suboptimal results.  ...  To this end, we propose a novel learning-to-rank framework, FULTR, that is the first to address both intrinsic and extrinsic reasons of unfairness when learning ranking policies from logged implicit feedback  ...  Recent and more direct approaches to dealing with position bias are counterfactual learning methods as proposed in [30, 44] .  ... 
arXiv:1911.08054v1 fatcat:ilmfz34dunbehp7ra6uyoq3a7a

A Large Scale Search Dataset for Unbiased Learning to Rank [article]

Lixin Zou, Haitao Mao, Xiaokai Chu, Jiliang Tang, Wenwen Ye, Shuaiqiang Wang, Dawei Yin
2022 arXiv   pre-print
The unbiased learning to rank (ULTR) problem has been greatly advanced by recent deep learning techniques and well-designed debias algorithms.  ...  tasks for ranking.  ...  Unbiased learning to rank (ULTR) is then proposed for debiasing user feedback with counterfactual learning algorithms (17; 28; 29) .  ... 
arXiv:2207.03051v1 fatcat:ggg5l2drdfhozn7y2sfmy6lziy

Position Bias Estimation for Unbiased Learning to Rank in Personal Search

Xuanhui Wang, Nadav Golbandi, Michael Bendersky, Donald Metzler, Marc Najork
2018 Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining - WSDM '18  
In contrast, the most recent work on unbiased learning-to-rank can effectively leverage the bias and thus focuses on estimating bias rather than relevance [20, 31] .  ...  The extracted bias can improve the learning-to-rank algorithms significantly. In addition, we compare the pointwise and pairwise learning-to-rank models.  ...  ACKNOWLEDGMENTS We thank Olivier Chapelle for his discussions on tradeoff among different position bias estimation methods and Roberto Bayardo for his suggestions on improving the paper presentation.  ... 
doi:10.1145/3159652.3159732 dblp:conf/wsdm/WangGBMN18 fatcat:shszbvbxpbeydit6ik5f5isn7q

Counterfactual Online Learning to Rank [chapter]

Shengyao Zhuang, Guido Zuccon
2020 Lecture Notes in Computer Science  
In this paper, we propose a counterfactual online learning to rank algorithm (COLTR) that combines the key components of both CLTR and OLTR.  ...  , logging ranker; and online learning to rank (OLTR), where a ranker is updated by recording user interaction with a result list produced by multiple rankers (usually via interleaving).  ...  Dr Guido Zuccon is the recipient of an Australian Research Council DECRA Research Fellowship (DE180101579) and a Google Faculty Award.  ... 
doi:10.1007/978-3-030-45439-5_28 fatcat:gpqp6bfqgza6tmd67sfdbqtyky

U-rank

Xinyi Dai, Jiawei Hou, Qing Liu, Yunjia Xi, Ruiming Tang, Weinan Zhang, Xiuqiang He, Jun Wang, Yong Yu
2020 Proceedings of the 29th ACM International Conference on Information & Knowledge Management  
Learning to rank with implicit feedback is one of the most important tasks in many real-world information systems where the objective is some specific utility, e.g., clicks and revenue.  ...  Moreover, our proposed U-rank has been deployed on a large-scale commercial recommender and a large improvement over the production baseline has been observed in an online A/B testing.  ...  ] and Lamb-daRank is a dual learning algorithm that jointly learns an unbiased ranker and an unbiased propensity model.  ... 
doi:10.1145/3340531.3412756 dblp:conf/cikm/DaiHLXT0H0020 fatcat:ejnob5uzdfakro6zl4xlmzzpuu
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