Evolution of Popularity Bias: Empirical Study and Debiasing

Understand

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.