research-article

FairGap: Fairness-Aware Recommendation via Generating Counterfactual Graph

Authors:

Feng XiaAuthors Info & Claims

ACM Transactions on Information Systems, Volume 42, Issue 4

Article No.: 94, Pages 1 - 25

https://doi.org/10.1145/3638352

Published: 09 February 2024 Publication History

Abstract

The emergence of Graph Neural Networks (GNNs) has greatly advanced the development of recommendation systems. Recently, many researchers have leveraged GNN-based models to learn fair representations for users and items. However, current GNN-based models suffer from biased user–item interaction data, which negatively impacts recommendation fairness. Although there have been several studies employing adversarial learning to mitigate this issue in recommendation systems, they mostly focus on modifying the model training approach with fairness regularization and neglect direct intervention of biased interaction. In contrast to these models, this article introduces a novel perspective by directly intervening in observed interactions to generate a counterfactual graph (called FairGap) that is not influenced by sensitive node attributes, enabling us to learn fair representations for users and items easily. We design FairGap to answer the key counterfactual question: “Would interactions with an item remain unchanged if a user’s sensitive attributes were concealed?”. We also provide theoretical proofs to show that our learning strategy via the counterfactual graph is unbiased in expectation. Moreover, we propose a fairness-enhancing mechanism to continuously improve user fairness in the graph-based recommendation. Extensive experimental results against state-of-the-art competitors and base models on three real-world datasets validate the effectiveness of our proposed model.

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Cited By

Zhao YWang YLiu YCheng XAggarwal CDerr T(2025)Fairness and Diversity in Recommender Systems: A SurveyACM Transactions on Intelligent Systems and Technology10.1145/366492816:1(1-28)Online publication date: 3-Jan-2025
https://dl.acm.org/doi/10.1145/3664928
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
Shrivastava RSisodia DNagwani N(2024)Multi-stakeholder recommendation system through deep learning-based preference evaluation and aggregation model with multi-view information embeddingInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10386261:6Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103862
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Index Terms

FairGap: Fairness-Aware Recommendation via Generating Counterfactual Graph
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 42, Issue 4

July 2024

751 pages

EISSN:1558-2868

DOI:10.1145/3613639

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 09 February 2024

Online AM: 22 December 2023

Accepted: 12 December 2023

Revised: 28 October 2023

Received: 28 June 2023

Published in TOIS Volume 42, Issue 4

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Cited By

Zhao YWang YLiu YCheng XAggarwal CDerr T(2025)Fairness and Diversity in Recommender Systems: A SurveyACM Transactions on Intelligent Systems and Technology10.1145/366492816:1(1-28)Online publication date: 3-Jan-2025
https://dl.acm.org/doi/10.1145/3664928
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
Shrivastava RSisodia DNagwani N(2024)Multi-stakeholder recommendation system through deep learning-based preference evaluation and aggregation model with multi-view information embeddingInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10386261:6Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103862
Guo ZSong PFeng CYao KDang CLiang J(2024)Causal intervention for knowledge graph denoising in recommender systemsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02500-0Online publication date: 21-Dec-2024
https://doi.org/10.1007/s13042-024-02500-0

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