FairGap: Fairness-Aware Recommendation via Generating Counterfactual Graph

doi:10.1145/3638352

	FairGap: Fairness-Aware Recommendation via Generating Counterfactual Graph
	Chen, Wei 1; Wu, Yiqing 2; Zhang, Zhao 2; Zhuang, Fuzhen 1,3; He, Zhongshi 4; Xie, Ruobing 5; Xia, Feng 5
	2024-07-01
发表期刊	ACM TRANSACTIONS ON INFORMATION SYSTEMS
ISSN	1046-8188
卷号	42 期号:4 页码:25
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.
关键词	Fairness recommendation graph neural network counterfactual
DOI	10.1145/3638352
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:001229267400007
出版者	ASSOC COMPUTING MACHINERY
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40055
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.