论文标题
建议系统中的反馈循环和偏差放大
Feedback Loop and Bias Amplification in Recommender Systems
论文作者
论文摘要
众所周知，推荐算法会受到流行偏见的困扰；在大多数其他项目被忽略时，经常建议一些受欢迎的物品。然后，这些建议被用户消耗，它们的反应将被记录并添加到系统中：通常称为反馈循环。在本文中，我们提出了一种模拟用户在离线环境中与推荐人互动的方法，并研究了反馈循环对几种推荐算法的流行偏差放大的影响。然后，我们展示了这种偏见放大如何导致其他几个问题，例如降低了总体多样性，从而改变了用户随着时间的流逝以及用户体验的同质化。特别是，我们表明，对于属于少数群体的用户，反馈循环的影响通常更强。
Recommendation algorithms are known to suffer from popularity bias; a few popular items are recommended frequently while the majority of other items are ignored. These recommendations are then consumed by the users, their reaction will be logged and added to the system: what is generally known as a feedback loop. In this paper, we propose a method for simulating the users interaction with the recommenders in an offline setting and study the impact of feedback loop on the popularity bias amplification of several recommendation algorithms. We then show how this bias amplification leads to several other problems such as declining the aggregate diversity, shifting the representation of users' taste over time and also homogenization of the users experience. In particular, we show that the impact of feedback loop is generally stronger for the users who belong to the minority group.
