Popularity Bias in Dynamic Recommendation
Popularity Bias in Dynamic Recommendation
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DOI：
10.1145/3447548.3467376
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Ziwei Zhu;Yun He;Xing Zhao;James Caverlee
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文献类型：
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作者：
Ziwei Zhu;Yun He;Xing Zhao;James Caverlee
流行度偏差是推荐系统中一个长期存在的挑战：热门物品被过度推荐，而用户可能感兴趣的不太热门的物品却推荐不足。这种偏差对用户和物品提供者都产生了不利影响，并且人们已经付出了很多努力来研究和解决这种偏差。然而，大多数现有的工作是在静态环境下研究流行度偏差，即仅针对记录数据的单轮推荐分析偏差。这些工作没有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没有答案：在动态场景中流行度偏差是如何演变的？动态推荐过程中的独特因素对偏差有什么影响？以及在这个长期的动态过程中如何去偏差？在这项工作中，我们研究动态推荐中的流行度偏差，旨在填补这些研究空白。具体来说，我们通过模拟实验进行了一项实证研究，以分析动态场景中的流行度偏差，并提出了一种动态去偏差策略和一种利用假阳性信号去偏差的新型假阳性校正方法，在大量实验中显示出了有效的性能。
Popularity bias is a long-standing challenge in recommender systems: popular items are overly recommended at the expense of less popular items that users may be interested in being under-recommended. 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 the popularity bias 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 investigate the popularity bias in dynamic recommendation and 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.