Popularity bias is an undesirable phenomenon associated with recommendation algorithms where popular items tend to be suggested over long-tail ones, even if the latter would be of reasonable interest for individuals. Such intrinsic tendencies of the recommenders may lead to producing ranked lists, in which items are not equally covered along the popularity tail. Although some recent studies aim to detect such biases of traditional algorithms and treat their effects on recommendations, the concept of popularity bias remains elusive for group recommender systems. Therefore, in this study, we focus on investigating popularity bias from the view of group recommender systems, which aggregate individual preferences to achieve recommendations for groups of users. We analyze various state-of-the-art aggregation techniques utilized in group recommender systems regarding their bias towards popular items. To counteract possible popularity issues in group recommendations, we adapt a traditional re-ranking approach that weighs items inversely proportional to their popularity within a group. Also, we propose a novel popularity bias mitigation procedure that re-ranks items by incorporating their popularity level and estimated group ratings in two distinct strategies. The first one aims to penalize popular items during the aggregation process highly and avoids bias better, while the second one puts more emphasis on group ratings than popularity and achieves a more balanced performance regarding conflicting goals of mitigating bias and boosting accuracy. Experiments performed on four real-world benchmark datasets demonstrate that both strategies are more efficient than the adapted approach, and empowering aggregation techniques with one of these strategies significantly decreases their bias towards popular items while maintaining reasonable ranking accuracy.

流行度偏差是与推荐算法相关的不良现象，在推荐算法中，倾向于建议使用长尾项而不是长尾项，即使后者对个人而言是合理的兴趣也是如此。推荐者的这种内在倾向可能会导致产生排名列表，在列表中，受欢迎程度尾部的条目没有被均等地覆盖。尽管最近的一些研究旨在检测传统算法的此类偏见并处理其对推荐的影响，但对于团体推荐系统而言，流行偏见的概念仍然难以捉摸。因此，在本研究中，我们集中于从组推荐系统的角度调查受欢迎度偏差，组推荐系统汇总了个人偏好以实现针对用户组的推荐。我们分析了团体推荐系统中使用的各种最新汇总技术，因为它们偏向热门项目。为了解决小组推荐中可能出现的受欢迎程度问题，我们采用了一种传统的重新排名方法，即权衡项目与小组中的受欢迎程度成反比。此外，我们提出了一种新颖的减少人气偏倚的程序，该程序通过将商品的受欢迎程度和估算的团体评分合并到两种不同的策略中来对商品重新排序。第一个目标是在汇总过程中对热门项目进行严厉的处罚，并更好地避免偏见，而第二个目标则是更加注重群体评级，而不是受欢迎程度，并且在缓解偏见和提高准确性的冲突目标方面实现了更为均衡的表现。