论文标题
潮流效应：不仅仅是另一种偏见
The Bandwagon Effect: Not Just Another Bias
论文作者
论文摘要
基于用户交互数据的优化推荐系统主要被视为处理选择偏差的问题，其中大多数现有工作都假设来自不同用户的交互是独立的。但是，已经表明，实际上用户反馈通常受到其他用户的早期交互的影响，例如通过平均评分，每项项目的视图或销售量等。这种现象被称为潮流效应。与以前的文献相反，我们认为潮流效应不应被视为统计偏见的问题。实际上，我们证明这种效果使单个相互作用及其样本平均无偏见。然而，我们表明它可以使估计量不一致，从而引入了一系列与相关性估计的融合的不同问题。我们的理论分析调查了潮流效应提出一致性问题的条件，并探讨了减轻这些问题的几种方法。这项工作旨在表明，潮流效应带来了一个不足的开放问题，从根本上讲，这与建议的选择偏见从根本上讲是不同的。
Optimizing recommender systems based on user interaction data is mainly seen as a problem of dealing with selection bias, where most existing work assumes that interactions from different users are independent. However, it has been shown that in reality user feedback is often influenced by earlier interactions of other users, e.g. via average ratings, number of views or sales per item, etc. This phenomenon is known as the bandwagon effect. In contrast with previous literature, we argue that the bandwagon effect should not be seen as a problem of statistical bias. In fact, we prove that this effect leaves both individual interactions and their sample mean unbiased. Nevertheless, we show that it can make estimators inconsistent, introducing a distinct set of problems for convergence in relevance estimation. Our theoretical analysis investigates the conditions under which the bandwagon effect poses a consistency problem and explores several approaches for mitigating these issues. This work aims to show that the bandwagon effect poses an underinvestigated open problem that is fundamentally distinct from the well-studied selection bias in recommendation.
