[Purpose/significance] The popularity bias of the personalized recommendation system is to recommend the most popular items rather than the most relevant ones to users. It is one of the important factors affecting fairness and has serious consequences for multiple stakeholders in recommendation systems. Accurately identifying popularity bias through scientific quantitative methods is of great theoretical and practical value for assessing the bias problems existing in current recommendation algorithms, helping to optimize the system and realizing trustworthy artificial intelli⁃ gence. [Method/process] Through the comparative analysis, the mainstream popularity bias measurement metrics in the current recommendation system field are compared. Based on this, a multidimensional measurement metrics frame⁃ work for discovering popularity bias in recommendation systems was constructed, and an empirical analysis was carried out. [Result/conclusion] From the six dimensions including diversity, personalization, user satisfaction, fairness, longterm benefits, and overall performance, the measurement method of popularity bias in personalized recommendation systems is clarified. The empirical results show that the measurement method has a certain degree of advancement in terms of wide coverage of indicators, high measurement accuracy, and strong scientific practice