research-article

Representation Matters When Learning From Biased Feedback in Recommendation

Authors:

Suhang WangAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 2220 - 2229

https://doi.org/10.1145/3511808.3557431

Published: 17 October 2022 Publication History

Abstract

The logged feedback for training recommender systems is usually subject to selection bias, which could not reflect real user preference. Thus, many efforts have been made to learn the de-biased recommender system from biased feedback. However, existing methods for dealing with selection bias are usually affected by the error of propensity weight estimation, have high variance, or assume access to uniform data, which is expensive to be collected in practice. In this work, we address these issues by proposing Learning De-biased Representations (LDR), a framework derived from the representation learning perspective. LDR bridges the gap between propensity weight estimation (WE) and unbiased weighted learning (WL) and provides an end-to-end solution that iteratively conducts WE and WL. We show LDR can effectively alleviate selection bias with bounded variance. We also perform theoretical analysis on the statistical properties of LDR, such as its bias, variance, and generalization performance. Extensive experiments on both semi-synthetic and real-world datasets demonstrate the effectiveness of LDR.

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Cited By

Guo ZWu ZXiao TAggarwal CLiu HWang S(2025)Counterfactual Learning on Graphs: A SurveyMachine Intelligence Research10.1007/s11633-024-1519-z22:1(17-59)Online publication date: 24-Jan-2025
https://doi.org/10.1007/s11633-024-1519-z
Wang XFukumoto FCui JSuzuki YLi JYu DHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)CaDRec: Contextualized and Debiased Recommender ModelProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657799(405-415)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657799
Chen ZXiao TWang DZhang M(2024)Pareto Graph Self-Supervised LearningICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447557(6630-6634)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10447557
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Index Terms

Representation Matters When Learning From Biased Feedback in Recommendation
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

Copyright © 2022 ACM.

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Published: 17 October 2022

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October 17 - 21, 2022

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Guo ZWu ZXiao TAggarwal CLiu HWang S(2025)Counterfactual Learning on Graphs: A SurveyMachine Intelligence Research10.1007/s11633-024-1519-z22:1(17-59)Online publication date: 24-Jan-2025
https://doi.org/10.1007/s11633-024-1519-z
Wang XFukumoto FCui JSuzuki YLi JYu DHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)CaDRec: Contextualized and Debiased Recommender ModelProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657799(405-415)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657799
Chen ZXiao TWang DZhang M(2024)Pareto Graph Self-Supervised LearningICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447557(6630-6634)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10447557
Xiao TChen ZWang SSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Reconsidering Learning Objectives in Unbiased Recommendation: A Distribution Shift PerspectiveProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599487(2764-2775)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599487
Jiang PXin KLi CZhou Y(2023)High-efficiency Device-Cloud Collaborative Transformer Model2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW59228.2023.00214(2204-2210)Online publication date: Jun-2023
https://doi.org/10.1109/CVPRW59228.2023.00214

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