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FairGap: Fairness-Aware Recommendation via Generating Counterfactual Graph

文献类型：期刊论文

作者Chen, Wei1; Wu, Yiqing2; Zhang, Zhao2; Zhuang, Fuzhen1,3; He, Zhongshi4; Xie, Ruobing5; Xia, Feng5
刊名ACM TRANSACTIONS ON INFORMATION SYSTEMS
出版日期2024-07-01
卷号42期号:4页码:25
关键词Fairness recommendation graph neural network counterfactual
ISSN号1046-8188
DOI10.1145/3638352
英文摘要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-ofthe-art competitors and base models on three real-world datasets validate the effectiveness of our proposed model.
资助项目National Key Research and Development Program of China[2021ZD0113602] ; National Natural Science Foundation of China[62206266] ; National Natural Science Foundation of China[62176014] ; Fundamental Research Funds for the Central Universities
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001229267400007
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/40054]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhuang, Fuzhen
作者单位1.Beihang Univ, Inst Artificial Intelligence, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
3.Zhongguancun Lab, Beijing, Peoples R China
4.Chongqing Univ, Coll Comp Sci, Chongqing, Peoples R China
5.Tencent, WeChat, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Wei,Wu, Yiqing,Zhang, Zhao,et al. FairGap: Fairness-Aware Recommendation via Generating Counterfactual Graph[J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS,2024,42(4):25.
APA Chen, Wei.,Wu, Yiqing.,Zhang, Zhao.,Zhuang, Fuzhen.,He, Zhongshi.,...&Xia, Feng.(2024).FairGap: Fairness-Aware Recommendation via Generating Counterfactual Graph.ACM TRANSACTIONS ON INFORMATION SYSTEMS,42(4),25.
MLA Chen, Wei,et al."FairGap: Fairness-Aware Recommendation via Generating Counterfactual Graph".ACM TRANSACTIONS ON INFORMATION SYSTEMS 42.4(2024):25.

入库方式： OAI收割

来源：计算技术研究所

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