research-article

Unbiased Sequential Recommendation with Latent Confounders

Authors:

Ji-Rong WenAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 2195 - 2204

https://doi.org/10.1145/3485447.3512092

Published: 25 April 2022 Publication History

Abstract

Sequential recommendation holds the promise of understanding user preference by capturing successive behavior correlations. Existing research focus on designing different models for better fitting the offline datasets. However, the observational data may have been contaminated by the exposure or selection biases, which renders the learned sequential models unreliable. In order to solve this fundamental problem, in this paper, we propose to reformulate the sequential recommendation task with the potential outcome framework, where we are able to clearly understand the data bias mechanism and correct it by re-weighting the training instances with the inverse propensity score (IPS). For more robustness modeling, a clipping strategy is applied to the IPS estimation to reduce the variance of the learning objective. To make our framework more practical, we design a parameterized model to remove the impact of the potential latent confounders. At last, we theoretically analyze the unbiasedness of the proposed framework under both vanilla and clipping IPS estimations. To the best of our knowledge, this is the first work on debiased sequential recommendation. We conduct extensive experiment based on both synthetic and real-world datasets to demonstrate the effectiveness of our framework.

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Cited By

Sun ZHe BShen SWang ZGong ZMa CQi QChen X(2025)Sequential Causal Effect Estimation by Jointly Modeling the Unmeasured Confounders and Instrumental VariablesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.351073437:2(910-922)Online publication date: Feb-2025
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Li CXie TYu CHu BLi ZCheng LKong BNiu D(2025)DGT: Unbiased sequential recommendation via Disentangled Graph TransformerKnowledge-Based Systems10.1016/j.knosys.2024.112946310(112946)Online publication date: Feb-2025
https://doi.org/10.1016/j.knosys.2024.112946
Bai TChen WYang CShi C(2025)Invariant debiasing learning for recommendation via biased imputationInformation Processing & Management10.1016/j.ipm.2024.10402862:3(104028)Online publication date: May-2025
https://doi.org/10.1016/j.ipm.2024.104028
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Unbiased Sequential Recommendation with Latent Confounders

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Publication History

Published: 25 April 2022

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Funding Sources

Renmin University of China
Major Innovation & Planning Interdisciplinary Platform for the Double-First Class Initiative
National Natural Science Foundation of China
Beijing Outstanding Young Scientist Program
Public Computing Cloud, Renmin University of China
Intelligent Social Governance Platform

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WWW '22

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Cited By

Sun ZHe BShen SWang ZGong ZMa CQi QChen X(2025)Sequential Causal Effect Estimation by Jointly Modeling the Unmeasured Confounders and Instrumental VariablesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.351073437:2(910-922)Online publication date: Feb-2025
https://doi.org/10.1109/TKDE.2024.3510734
Li CXie TYu CHu BLi ZCheng LKong BNiu D(2025)DGT: Unbiased sequential recommendation via Disentangled Graph TransformerKnowledge-Based Systems10.1016/j.knosys.2024.112946310(112946)Online publication date: Feb-2025
https://doi.org/10.1016/j.knosys.2024.112946
Bai TChen WYang CShi C(2025)Invariant debiasing learning for recommendation via biased imputationInformation Processing & Management10.1016/j.ipm.2024.10402862:3(104028)Online publication date: May-2025
https://doi.org/10.1016/j.ipm.2024.104028
Tang MCui SJin ZLiang SLi CZou L(2025)Sequential recommendation by reprogramming pretrained transformerInformation Processing & Management10.1016/j.ipm.2024.10393862:1(103938)Online publication date: Jan-2025
https://doi.org/10.1016/j.ipm.2024.103938
Niu YXing XJia ZLiu RXin M(2025)Implicit local–global feature extraction for diffusion sequence recommendationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109471139(109471)Online publication date: Jan-2025
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An YTan YSun XFerrari G(2024)Recommender System: A Comprehensive Overview of Technical Challenges and Social ImplicationsIECE Transactions on Sensing, Communication, and Control10.62762/TSCC.2024.8985031:1(30-51)Online publication date: 15-Oct-2024
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Krause TDeriyeva ABeinke JBartels GThomas O(2024)Mitigating Exposure Bias in Recommender Systems—A Comparative Analysis of Discrete Choice ModelsACM Transactions on Recommender Systems10.1145/36412913:2(1-37)Online publication date: 27-Jan-2024
https://dl.acm.org/doi/10.1145/3641291
Gao CZheng YWang WFeng FHe XLi Y(2024)Causal Inference in Recommender Systems: A Survey and Future DirectionsACM Transactions on Information Systems10.1145/363904842:4(1-32)Online publication date: 9-Feb-2024
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Cavenaghi EZanga AStella FZanker M(2024)Towards a Causal Decision-Making Framework for Recommender SystemsACM Transactions on Recommender Systems10.1145/36291692:2(1-34)Online publication date: 14-May-2024
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Zhang KShi TDai SZhang XLi YLu JZang XSong YXu JSerra ESpezzano F(2024)SAQRec: Aligning Recommender Systems to User Satisfaction via Questionnaire FeedbackProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679643(3165-3175)Online publication date: 21-Oct-2024
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