Aggregation of consumer ratings: an application to Yelp.com

Weijia (Daisy) Dai (), Ginger Jin (), Jungmin Lee and Michael Luca ()
Additional contact information
Weijia (Daisy) Dai: Lehigh University
Ginger Jin: University of Maryland & NBER
Michael Luca: Harvard Business School

Quantitative Marketing and Economics (QME), 2018, vol. 16, issue 3, No 2, 289-339

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: D8 L15 L86 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

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Related works:
Working Paper: Aggregation of Consumer Ratings: An Application to Yelp.com (2017)
Working Paper: Aggregation of Consumer Ratings: An Application to Yelp.com (2012)
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DOI: 10.1007/s11129-017-9194-9

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