Given the sharply increasing number of online reviews, the selection of strategies by review-hosting firms to help users access more helpful reviews is an intriguing but insufficiently studied issue. We first propose a model to help us understand how reviews receive helpful votes (HV) and non-helpful votes. According to this model, the performances of different ranking approaches are compared using several simulated datasets with empirical features. In addition to three well-known ranking approaches, we develop a novel approach based on Bayesian statistics that is easy to implement in existing websites and can be combined with other content recommendation techniques to determine the prior belief in online reviews. More importantly, we suggest two simple ways to enhance existing ranking approaches. The numerical evidence demonstrates the advantages of two enhanced approaches, as indicated by higher helpful ratios and a reduced Matthew effect. These findings have important practical implications for consumers, online retailers, and review-hosting firms.

鉴于在线评论的数量急剧增加，评论托管公司选择的策略来帮助用户访问更有用的评论是一个引人入胜但研究不足的问题。我们首先提出一个模型，以帮助我们了解评论如何获得有帮助的投票（HV）和无帮助的投票。根据此模型，使用具有经验特征的几个模拟数据集比较了不同排名方法的性能。除了三种著名的排名方法外，我们还基于贝叶斯统计数据开发了一种新颖的方法，该方法易于在现有网站中实施，并且可以与其他内容推荐技术结合使用以确定在线评论的先验信念。更重要的是，我们提出了两种简单的方法来增强现有的排名方法。数值证据证明了两种增强方法的优势，如较高的有用率和较低的马修效应所表明。这些发现对消费者，在线零售商和评论托管公司具有重要的实际意义。