Aggregation of Consumer Ratings: An Application to Yelp.Com

59 Pages Posted: 30 Nov 2012 Last revised: 27 Mar 2022

See all articles by Daisy Dai

Daisy Dai

Purdue University, Krannert School of Management

Ginger Zhe Jin

University of Maryland - Department of Economics; National Bureau of Economic Research (NBER)

Jungmin Lee

Seoul National University - Department of Economics

Michael Luca

Harvard University - Business School (HBS)

Multiple version iconThere are 2 versions of this paper

Date Written: November 2012

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.

Suggested Citation

Dai, Daisy and Jin, Ginger Zhe and Lee, Jungmin and Luca, Michael, Aggregation of Consumer Ratings: An Application to Yelp.Com (November 2012). NBER Working Paper No. w18567, Available at SSRN: https://ssrn.com/abstract=2183028

Daisy Dai (Contact Author)

Purdue University, Krannert School of Management ( email )

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Ginger Zhe Jin

University of Maryland - Department of Economics ( email )

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National Bureau of Economic Research (NBER)

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Jungmin Lee

Seoul National University - Department of Economics ( email )

Michael Luca

Harvard University - Business School (HBS) ( email )

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United States

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