  Popularity bias is a long-standing challenge in recommender systems. Such a
bias exerts detrimental impact on both users and item providers, and many
efforts have been dedicated to studying and solving such a bias. However, most
existing works situate this problem in a static setting, where the bias is
analyzed only for a single round of recommendation with logged data. These
works fail to take account of the dynamic nature of real-world recommendation
process, leaving several important research questions unanswered: how does the
popularity bias evolve in a dynamic scenario? what are the impacts of unique
factors in a dynamic recommendation process on the bias? and how to debias in
this long-term dynamic process? In this work, we aim to tackle these research
gaps. Concretely, we conduct an empirical study by simulation experiments to
analyze popularity bias in the dynamic scenario and propose a dynamic debiasing
strategy and a novel False Positive Correction method utilizing false positive
signals to debias, which show effective performance in extensive experiments.
