Aggregation of Consumer Ratings: An Application to Yelp.com

Weijia Dai (), Ginger Jin (), Jungmin Lee and Michael Luca ()
Additional contact information
Weijia Dai: University of Maryland
Ginger Jin: University of Maryland
Michael Luca: Harvard Business School, Negotiation, Organizations & Markets unit

No 13-042, Harvard Business School Working Papers from Harvard Business School

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)
Pages: 41 pages
Date: 2012-11, Revised 2017-11
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.hbs.edu/faculty/pages/download.aspx?name=13-042.pdf Revised version, 2017 (application/pdf)

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 (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:hbs:wpaper:13-042

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