Due to the extensive growth of information available online, recommender systems play a more significant role in serving people's interests. Traditional recommender systems mostly use an accuracy-focused approach to produce recommendations. Today's research suggests that this single-dimension approach can lead the system to be biased against a series of items with certain attributes. Biased recommendations across groups of items can endanger the interests of item providers along with causing user dissatisfaction with the system. This study aims to manage a new type of intersectional bias regarding the geographical origin and popularity of items in the output of state-of-the-art collaborative filtering recommender algorithms. We introduce an algorithm called MFAIR, a multi-facet post-processing bias mitigation algorithm to alleviate these biases. Extensive experiments on two real-world datasets of movies and books, enriched with the items' continents of production, show that the proposed algorithm strikes a reasonable balance between accuracy and both types of the mentioned biases. According to the results, our proposed approach outperforms a well-known competitor with no or only a slight loss of efficiency.

翻译：由于在线信息的大量增长，推荐系统在满足用户兴趣方面发挥着越来越重要的作用。传统推荐系统大多采用以准确性为中心的方法来生成推荐。现今的研究表明，这种单一维度的方法可能导致系统对具有某些属性的系列项目产生偏见。针对项目群组的偏见推荐不仅会危及项目提供商的利益，还会引发用户对系统的不满。本研究旨在管理先进协同过滤推荐算法输出中关于项目地理来源和流行度的新型交叉偏差。我们提出了一种名为MFAIR的多维度后处理偏差缓解算法，以减轻这些偏差。在包含项目制作大洲信息的两个真实世界数据集（电影和书籍）上进行的广泛实验表明，所提出的算法在准确性与上述两种类型的偏差之间取得了合理的平衡。结果显示，我们的方法在效率无损失或仅轻微损失的情况下，优于知名的对比算法。