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Towards Unbiased and Robust Causal Ranking for Recommender Systems

Authors:

Suhang WangAuthors Info & Claims

WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Pages 1158 - 1167

https://doi.org/10.1145/3488560.3498521

Published: 15 February 2022 Publication History

Abstract

We study the problem of optimizing ranking metrics with unbiased and robust causal estimation for recommender systems. A user may click/purchase an item regardless of whether the item is recommended or not. Thus, it is important to estimate the causal effect of recommendation and rank items higher with a larger causal effect. However, most existing works focused on improving the accuracy of recommendations, which usually have large bias and variance. Therefore, in this paper, we provide a general and theoretically rigorous framework for causal recommender systems, which enables unbiased evaluation and learning for the ranking metrics with confounding bias. We first propose a robust estimator for unbiased ranking evaluation and theoretically show that this estimator has a smaller bias and variance. We then propose a deep variational information bottleneck (IB) approach to exploit the sufficiency of the propensity score for estimation adjustment and better generalization. We also provide the learning bound and develop an unbiased learning algorithm to optimize the causal metric. Results on semi-synthetic and real-world datasets show that our evaluation and learning algorithms significantly outperform existing methods.

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We study the problem of optimizing ranking metrics for causal recommender systems.

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Zhu YYi JXie JChen Z(2024)Deep Causal Reasoning for RecommendationsACM Transactions on Intelligent Systems and Technology10.1145/365398515:4(1-25)Online publication date: 18-Jun-2024
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Guarrasi VSiciliano FSilvestri F(2024)RobustRecSys @ RecSys2024: Design, Evaluation and Deployment of Robust Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687106(1265-1269)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3687106
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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Index Terms

Towards Unbiased and Robust Causal Ranking for Recommender Systems
1. Computing methodologies
  1. Machine learning

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cover image ACM Conferences

WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

February 2022

1690 pages

ISBN:9781450391320

DOI:10.1145/3488560

General Chairs:
K. Selcuk Candan
Arizona State University, USA
,
Huan Liu
Arizona State University, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Xin Luna Dong
Meta Platforms, Inc. (former Facebook), USA
,
Jiliang Tang
Michigan State University, USA

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Published: 15 February 2022

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February 21 - 25, 2022

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Cited By

Zhu YYi JXie JChen Z(2024)Deep Causal Reasoning for RecommendationsACM Transactions on Intelligent Systems and Technology10.1145/365398515:4(1-25)Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1145/3653985
Guarrasi VSiciliano FSilvestri F(2024)RobustRecSys @ RecSys2024: Design, Evaluation and Deployment of Robust Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687106(1265-1269)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3687106
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
https://dl.acm.org/doi/10.1145/3639048
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
https://dl.acm.org/doi/10.1145/3629169
Li XLiang SLei YLi CHou YZheng DMa TSerra ESpezzano F(2024)CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient Health StateProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679542(1276-1285)Online publication date: 21-Oct-2024
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Liang SLi XMu SLi CLei YHou YMa T(2024)CIDGMed: Causal Inference-Driven Medication Recommendation with enhanced dual-granularity learningKnowledge-Based Systems10.1016/j.knosys.2024.112685(112685)Online publication date: Nov-2024
https://doi.org/10.1016/j.knosys.2024.112685
Liu BZeng JWen JGao MZhou W(2024)CBRec: A causal way balancing multidimensional attraction effect in POI recommendationsKnowledge-Based Systems10.1016/j.knosys.2024.112607305(112607)Online publication date: Dec-2024
https://doi.org/10.1016/j.knosys.2024.112607
Li PZhu XSu X(2024)Neural_BPRElectronic Commerce Research and Applications10.1016/j.elerap.2023.10132362:COnline publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1016/j.elerap.2023.101323
Gan YWang QHuang ZYang L(2024)Attention-based causal representation learning for out-of-distribution recommendationApplied Intelligence10.1007/s10489-024-05835-x54:24(12964-12978)Online publication date: 12-Oct-2024
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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
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