Mitigating Confounding Bias in Practical Recommender Systems With Partially Inaccessible Exposure Status

doi:10.1109/TPAMI.2023.3327411

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	Mitigating Confounding Bias in Practical Recommender Systems With Partially Inaccessible Exposure Status
	Cao, Tianwei 1; Xu, Qianqian 2; Yang, Zhiyong 1; Huang, Qingming 1,2,3,4
	2024-02-01
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN	0162-8828
卷号	46 期号:2 页码:957-974
摘要	To improve user experience, recommender systems have been widely used on many online platforms. In these systems, recommendation models are typically learned from positive/negative feedback that are collected automatically. Notably, recommender systems are a little different from general supervised learning tasks. In recommender systems, there are some factors (e.g., previous recommendation models or operation strategies of a online platform) that determine which items can be exposed to each individual user. Normally, the previous exposure results are not only relevant to the instances' features (i.e., user or item), but also affect their feedback ratings, thus leading to confounding bias in the recommendation models. To mitigate this bias, researchers have already provided a variety of strategies. However, there are still two issues that are underappreciated: 1) previous debiased RS approaches cannot effectively capture recommendation-specific, exposure-specific and their common knowledge simultaneously; 2) the true exposure results of the user-item pairs are partially inaccessible, so there would be some noises if we use their observability to approximate it as existing approaches. Motivated by this, we develop a novel debiasing recommendation approach. More specifically, we first propose a mutual information-based counterfactual learning framework based on the causal relationship among the instance features, exposure status, and ratings. This framework can 1) capture recommendation-specific, exposure-specific and their common knowledge by explicitly modeling the relationship among the causal factors, and 2) achieve robustness towards partially inaccessible exposure results by a pairwise learning strategy. Under such a framework, we implement an optimizable loss function with theoretical analysis. By minimizing this loss, we expect to obtain an unbiased recommendation model that reflects the users' real interests. Meanwhile, we also prove that our loss function has robustness towards the partial inaccessibility of the exposure status. Finally, extensive experiments on public datasets manifest the superiority of our proposed method in boosting the recommendation performance.
关键词	Recommender system collaborative filtering confounding bias debias counterfactual learning
DOI	10.1109/TPAMI.2023.3327411
收录类别	SCI
语种	英语
资助项目	National Key R#x0026;D Program of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:001140839000039
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38408
专题	中国科学院计算技术研究所
通讯作者	Xu, Qianqian; Huang, Qingming
作者单位	1.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management BDKM, Beijing 101408, Peoples R China 4.Peng Cheng Lab, Shenzhen 518055, Peoples R China
推荐引用方式 GB/T 7714	Cao, Tianwei,Xu, Qianqian,Yang, Zhiyong,et al. Mitigating Confounding Bias in Practical Recommender Systems With Partially Inaccessible Exposure Status[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2024,46(2):957-974.
APA	Cao, Tianwei,Xu, Qianqian,Yang, Zhiyong,&Huang, Qingming.(2024).Mitigating Confounding Bias in Practical Recommender Systems With Partially Inaccessible Exposure Status.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,46(2),957-974.
MLA	Cao, Tianwei,et al."Mitigating Confounding Bias in Practical Recommender Systems With Partially Inaccessible Exposure Status".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 46.2(2024):957-974.