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Quantitative Marketing and Economics

, Volume 16, Issue 3, pp 289–339 | Cite as

Aggregation of consumer ratings: an application to Yelp.com

  • Weijia (Daisy) Dai
  • Ginger Jin
  • Jungmin Lee
  • Michael Luca
Article
  • 337 Downloads

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.

Keywords

User generated content Crowdsourcing E-commerce Learning Yelp 

JEL Classification

D8 L15 L86 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Lehigh UniversityBethlehemUSA
  2. 2.University of Maryland & NBERCollege ParkUSA
  3. 3.Seoul National UniversitySeoulSouth Korea
  4. 4.Harvard Business SchoolBostonUSA

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