让推荐系统更公平：用原型法减少文化偏见
Embedding Cultural Diversity in Prototype-based Recommender Systems
- 筛选无关原型，优化嵌入空间表示
- 27%提升冷门商品排名，2%改善小国内容排名
- 兼顾公平与效果，适合文化多样性研究者
推荐系统中的流行度偏差会加剧主流文化过代表、边缘群体被忽视的问题，尤其影响文化产品平台。本文针对原型基矩阵分解方法中的人口统计学偏差，以国家原籍作为文化身份代理，通过改进嵌入空间学习过程缓解该问题。首先，过滤无关原型以提升代表性；其次，引入正则化技术使原型在嵌入空间中分布更均匀。在四个数据集上，模型使长尾商品平均排名提升27%，来自未充分代表国家的商品平均排名提升2%。同时，HitRatio@10相比当前最优模型提升2%，表明公平性增强不牺牲推荐质量。原型分布更合理，也带来更具包容性的解释。
原文摘要 · Abstract (English)
Popularity bias in recommender systems can increase cultural overrepresentation by favoring norms from dominant cultures and marginalizing underrepresented groups. This issue is critical for platforms offering cultural products, as they influence consumption patterns and human perceptions. In this work, we address popularity bias by identifying demographic biases within prototype-based matrix factorization methods. Using the country of origin as a proxy for cultural identity, we link this demographic attribute to popularity bias by refining the embedding space learning process. First, we propose filtering out irrelevant prototypes to improve representativity. Second, we introduce a regularization technique to enforce a uniform distribution of prototypes within the embedding space. Across four datasets, our results demonstrate a 27\% reduction in the average rank of long-tail items and a 2\% reduction in the average rank of items from underrepresented countries. Additionally, our model achieves a 2\% improvement in HitRatio@10 compared to the state-of-the-art, highlighting that fairness is enhanced without compromising recommendation quality. Moreover, the distribution of prototypes leads to more inclusive explanations by better aligning items with diverse prototypes.
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