Aggregation of Consumer Ratings: An Application to Yelp.com
NBER Working Paper No. 18567
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.
Document Object Identifier (DOI): 10.3386/w18567
Published: Weijia Dai & Ginger Jin & Jungmin Lee & Michael Luca, 2018. "Aggregation of consumer ratings: an application to Yelp.com," Quantitative Marketing and Economics, vol 16(3), pages 289-339.
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