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research-article

Addressing Confounding Feature Issue for Causal Recommendation

Authors:

Chonggang Song,

Yongdong ZhangAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 3

Article No.: 53, Pages 1 - 23

https://doi.org/10.1145/3559757

Published: 07 February 2023 Publication History

Abstract

In recommender systems, some features directly affect whether an interaction would happen, making the happened interactions not necessarily indicate user preference. For instance, short videos are objectively easier to finish even though the user may not like the video. We term such feature as confounding feature, and video length is a confounding feature in video recommendation. If we fit a model on such interaction data, just as done by most data-driven recommender systems, the model will be biased to recommend short videos more, and deviate from user actual requirement.

This work formulates and addresses the problem from the causal perspective. Assuming there are some factors affecting both the confounding feature and other item features, e.g., the video creator, we find the confounding feature opens a backdoor path behind user-item matching and introduces spurious correlation. To remove the effect of backdoor path, we propose a framework named Deconfounding Causal Recommendation(DCR), which performs intervened inference with do-calculus. Nevertheless, evaluating do-calculus requires to sum over the prediction on all possible values of confounding feature, significantly increasing the time cost. To address the efficiency challenge, we further propose a mixture-of-experts (MoE) model architecture, modeling each value of confounding feature with a separate expert module. Through this way, we retain the model expressiveness with few additional costs. We demonstrate DCR on the backbone model of neural factorization machine (NFM), showing that DCR leads to more accurate prediction of user preference with small inference time cost. We release our code at: https://github.com/zyang1580/DCR.

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Cited By

Wang LXu MZhang QShi YWu Q(2025)Causal disentanglement for regulating social influence bias in social recommendationNeurocomputing10.1016/j.neucom.2024.129133618(129133)Online publication date: Feb-2025
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Zhang CYao RQin SLi ZAgrawal SMishra BTran TMa MLin QChintalapati MZhang D(2024)Deoxys: A Causal Inference Engine for Unhealthy Node Mitigation in Large-scale Cloud InfrastructureProceedings of the 2024 ACM Symposium on Cloud Computing10.1145/3698038.3698534(361-379)Online publication date: 20-Nov-2024
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Index Terms

Addressing Confounding Feature Issue for Causal Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 41, Issue 3

July 2023

890 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3582880

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 07 February 2023

Online AM: 30 August 2022

Accepted: 29 July 2022

Revised: 28 July 2022

Received: 10 May 2022

Published in TOIS Volume 41, Issue 3

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National Key Research and Development Program of China
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Cited By

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https://doi.org/10.1016/j.neucom.2024.129133
Zhang CYao RQin SLi ZAgrawal SMishra BTran TMa MLin QChintalapati MZhang D(2024)Deoxys: A Causal Inference Engine for Unhealthy Node Mitigation in Large-scale Cloud InfrastructureProceedings of the 2024 ACM Symposium on Cloud Computing10.1145/3698038.3698534(361-379)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3698038.3698534
He LHuang ZLiu CLi RWu RLiu QChen ECai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)One-bit Deep Hashing: Towards Resource-Efficient Hashing Model with Binary Neural NetworkProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681496(7162-7171)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681496
Shao PWu LZhang KLian DHong RLi YWang M(2024)Average User-Side Counterfactual Fairness for Collaborative FilteringACM Transactions on Information Systems10.1145/365663942:5(1-26)Online publication date: 13-May-2024
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Dong LHe GSun A(2024)Not All Videos Become Outdated: Short-Video Recommendation by Learning to Deconfound Release Interval BiasProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688113(179-188)Online publication date: 8-Oct-2024
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Zhao HCai GZhu JDong ZXu JWen JBaeza-Yates RBonchi F(2024)Counteracting Duration Bias in Video Recommendation via Counterfactual Watch TimeProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671817(4455-4466)Online publication date: 25-Aug-2024
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