论文标题
受欢迎程度的演变偏见：实证研究和辩护
Evolution of Popularity Bias: Empirical Study and Debiasing
论文作者
论文摘要
在推荐系统中，受欢迎程度偏见是一个长期的挑战。这样的偏见会对用户和物品提供商施加不利影响，并且许多努力致力于研究和解决这种偏见。但是，大多数现有作品都将此问题置于静态环境中，在静态环境中，仅分析偏差，仅用于一轮使用已记录的数据的推荐。这些作品未能考虑到现实世界推荐过程的动态性质，留下了几个重要的研究问题：在动态场景中，受欢迎程度偏见如何发展？在动态推荐过程中，独特因素对偏见有什么影响？以及如何在这个长期动态过程中进行辩论？在这项工作中，我们旨在解决这些研究差距。具体而言，我们通过模拟实验进行了一项经验研究，以分析动态场景中的受欢迎程度偏差，并提出了动态的偏见策略，并采用了一种利用假信号对Debias进行的虚假阳性校正方法，在广泛的实验中表现出有效的表现。
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.
