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.

翻译：推荐系统中的流行度偏差会加剧文化过度代表现象，即偏向主流文化规范并边缘化少数群体。对于提供文化产品的平台而言，这一问题至关重要，因为它们影响着消费模式与人类认知。本研究通过识别基于原型的矩阵分解方法中的人口统计偏差来应对流行度偏差问题。我们以来源国作为文化身份的代理变量，通过改进嵌入空间学习过程，将这一人口统计属性与流行度偏差相关联。首先，我们提出过滤无关原型以提高代表性。其次，我们引入正则化技术以强制原型在嵌入空间中均匀分布。在四个数据集上的实验结果表明：长尾物品的平均排名降低了27%，来自代表性不足国家的物品平均排名降低了2%。此外，与现有最优方法相比，我们的模型在HitRatio@10指标上实现了2%的提升，这表明在提升公平性的同时并未牺牲推荐质量。更重要的是，原型分布通过使物品与多样化原型更好对齐，产生了更具包容性的解释。