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FairCoRe: Fairness-Aware Recommendation Through Counterfactual representation learning

IEEE Transactions on Knowledge and Data Engineering 2025年第7期37卷 4049-4062页

作者： Bin, Chenzhong Liu, Wenqiang Zhang, Feng Chang, Liang Gu, Tianlong Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Guilin University of Electronic Technology School of Business Guilin541004 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China Jinan University College of Cyber Security Engineering Research Center of Trustworthy AI Ministry of Education Guangzhou510632 China

Eliminating bias from data representations is crucial to ensure fairness in recommendation. Existing studies primarily focus on weakening the correlation between data representations and sensitive attributes, yet may inadvertently steer the user representations toward another potential bias direction of the target attribute. Furthermore, they often overlook the impact of user preferences on capturing sensitive information, incurring inadequate bias elimination. In this paper, we propose a Fair Counterfactual representations (FairCoRe) learning framework, which aims to ensure the neutrality of representations among all bias directions. First, we intervene on sensitive attributes to construct a counterfactual scenario. Then, two opposing attribute prediction tasks are respectively performed in ground-truth and counterfactual scenarios to encode sensitive information along different bias directions. Second, we design a bias-aware enhancement learning method that quantifies the respective correlation of user preferences and sensitive attributes to enhance sensitive information encoding. Finally, we introduce two mutual information optimization methods that optimize the representations to capture users’ interests and disentangle sensitive factors. Moreover, we propose an attribute neutralization strategy that refines the learned representations, ensuring sensitive attribute neutrality. Extensive experiments demonstrate that our method achieves the optimal fairness and competitive accuracy compared to state-of-the-art methods. © 1989-2012 IEEE.

关键词： Accuracy Mutual Information Correlation Data Visualization Medical Services Training Measurement Disentangled representation learning Semantics Recommender Systems Recommender System Recommendation Fairness representation learning Counterfactual representations Potential Bias Mutual Information User Preferences Sensitive Attributes Counterfactual Scenario User representation Time Complexity Generative Adversarial Networks learning Objectives Demographic Groups Baseline methods Recommender Systems Minimum Method Prediction Loss Preference Information Graph Convolution User Side Recommendation Model Counterfactual Thinking Maximum Mutual Information representation learning methods Maximization Method Recommendation Accuracy Recommendation Method Recommendation Task Notions Of Fairness Negative Samples Mutual Information Estimation

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