Global popularity (GP) bias is the phenomenon that popular items are recommended much more frequently than they should be, which goes against the goal of providing personalized recommendations and harms user experience and recommendation accuracy. Many methods have been proposed to reduce GP bias but they fail to notice the fundamental problem of GP, i.e., it considers popularity from a \textit{global} perspective of \textit{all users} and uses a single set of popular items, and thus cannot capture the interests of individual users. As such, we propose a user-aware version of item popularity named \textit{personal popularity} (PP), which identifies different popular items for each user by considering the users that share similar interests. As PP models the preferences of individual users, it naturally helps to produce personalized recommendations and mitigate GP bias. To integrate PP into recommendation, we design a general \textit{personal popularity aware counterfactual} (PPAC) framework, which adapts easily to existing recommendation models. In particular, PPAC recognizes that PP and GP have both direct and indirect effects on recommendations and controls direct effects with counterfactual inference techniques for unbiased recommendations. All codes and datasets are available at \url{https://github.com/Stevenn9981/PPAC}.

翻译：全球流行度（GP）偏差是指热门项目被推荐频率远高于应有水平的现象，这违背了提供个性化推荐的目标，损害了用户体验和推荐准确性。虽然已有多种方法被提出以缓解GP偏差，但它们未能认识到GP的根本问题——即从所有用户的全局视角考虑流行度，并使用单一的热门项目集合，因而无法捕捉个体用户的兴趣。为此，我们提出一种面向用户的项目流行度变体——个性化流行度（PP），通过考虑具有相似兴趣的用户，为每个用户识别不同的热门项目。由于PP建模了个体用户的偏好，它自然有助于产生个性化推荐并缓解GP偏差。为了将PP集成到推荐中，我们设计了一个通用的个性化流行度感知反事实（PPAC）框架，该框架易于适配现有推荐模型。具体而言，PPAC识别出PP和GP对推荐存在直接和间接影响，并通过反事实推理技术控制直接影响，从而实现无偏推荐。所有代码和数据集可在\url{https://github.com/Stevenn9981/PPAC}获取。