用公平性约束提升论文推荐中的少数群体参与度，兼顾质量与公正。
Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendation
- 基于MLP模型，通过交叉公平性损失减少作者种族、国家等维度的偏见。
- 在SIGCHI等会议数据上，少数群体参与率提升42.03%，整体效用增3.16%。
- 适合关注学术评审公平性、期刊/会议推荐系统优化的研究者。
尽管采用双盲评审，作者的种族、国家等人口统计学特征仍会带来偏见，使少数群体处于不利地位。本文提出Fair-PaperRec，一种基于多层感知机（MLP）的模型，在维护高质量标准的同时缓解论文录用决策中的代际差异。该方法通过交叉公平性标准（如种族、国家）和定制化公平性损失函数，抑制偏见，区别于启发式手段。基于ACM SIGCHI、DIS和IUI三个会议的数据评估显示，少数群体参与度提升42.03%，整体效用提高3.16%，表明促进多样性不会牺牲学术严谨性，支持以公平为导向的同行评审解决方案。
原文摘要 · Abstract (English)
Despite frequent double-blind review, demographic biases of authors still disadvantage the underrepresented groups. We present Fair-PaperRec, a MultiLayer Perceptron (MLP)-based model that addresses demographic disparities in post-review paper acceptance decisions while maintaining high-quality requirements. Our methodology penalizes demographic disparities while preserving quality through intersectional criteria (e.g., race, country) and a customized fairness loss, in contrast to heuristic approaches. Evaluations using conference data from ACM Special Interest Group on Computer-Human Interaction (SIGCHI), Designing Interactive Systems (DIS), and Intelligent User Interfaces (IUI) indicate a 42.03% increase in underrepresented group participation and a 3.16% improvement in overall utility, indicating that diversity promotion does not compromise academic rigor and supports equity-focused peer review solutions.
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