区分流行度与质量，让冷门好内容也能被推荐。
Disentangling Popularity and Quality: An Edge Classification Approach for Fair Recommendation
- 用边分类方法分离物品的流行度和真实质量
- 公平性指标平均提升约32%，准确率接近顶尖水平
- 适合关注推荐公平性的研究者与产品设计者
图神经网络（GNN）在推荐系统中表现出色，但常受流行度偏差影响，使高频交互物品占据优势，忽视高质量但不热门的项目。本文提出一种基于GNN的推荐模型，通过边分类技术区分流行度偏差与真实的质量差异，引入成本敏感学习调整误分类惩罚，避免低曝光但相关项目被忽略。实验表明，在不同场景下公平性指标平均提升约32%，同时保持与最先进方法相当的准确性，仅略有波动。
原文摘要 · Abstract (English)
Graph neural networks (GNNs) have proven to be an effective tool for enhancing the performance of recommender systems. However, these systems often suffer from popularity bias, leading to an unfair advantage for frequently interacted items, while overlooking high-quality but less popular items. In this paper, we propose a GNN-based recommendation model that disentangles popularity and quality to address this issue. Unlike existing methods that treat all long-tail items uniformly, our approach introduces an edge classification technique to differentiate between popularity bias and genuine quality disparities among items. Furthermore, it uses cost-sensitive learning to adjust the misclassification penalties, ensuring that underrepresented yet relevant items are not unfairly disregarded. Experimental results demonstrate improvements in fairness metrics by approximately $32\%$ on average across different scenarios while maintaining competitive accuracy, with only minor variations compared to state-of-the-art methods.
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