Aggregation of Consumer Ratings: An Application to Yelp.com

Weijia Dai, Ginger Z. Jin, Jungmin Lee and Michael Luca

No 18567, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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.

JEL-codes: D8 L15 L86 (search for similar items in EconPapers)
Date: 2012-11
Note: IO
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Published as Weijia (Daisy) Dai & Ginger Jin & Jungmin Lee & Michael Luca, 2018. "Aggregation of consumer ratings: an application to Yelp.com," Quantitative Marketing and Economics (QME), Springer, vol. 16(3), pages 289-339, September.

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Related works:
Journal Article: Aggregation of consumer ratings: an application to Yelp.com (2018)
Working Paper: Aggregation of Consumer Ratings: An Application to Yelp.com (2017)
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