Because consumer reviews leverage the wisdom of the crowd, the way in which they are aggregated is a central decision faced by platforms. We explore this “rating aggregation problem” and offer a structural approach to solving it, allowing for (1) reviewers to vary in stringency and accuracy, (2) reviewers to be influenced by existing reviews, and (3) product quality to change over time. Applying this to restaurant reviews from Yelp.com, we construct an adjusted average rating and show that even a simple algorithm can lead to large information efficiency gains relative to the arithmetic average.

中文翻译：

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消费者评分汇总：Yelp.com的应用程序

因为消费者评论会利用人群的智慧，所以汇总评论的方式是平台面临的中心决策。我们探索了这个“评级汇总问题”，并提供了一种结构化的解决方案，允许（1）评论者的严格性和准确性有所不同，（2）评论者会受到现有评论的影响，并且（3）产品质量会发生变化时间。将其应用于Yelp.com的餐厅评论，我们构建了调整后的平均评分，并表明，即使是简单的算法也可以相对于算术平均值获得较大的信息效率。