Aggregation of Consumer Ratings: An Application to Yelp.com
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.
Previously circulated as "Optimal Aggregation of Consumer Ratings: An Application to Yelp.com." We are grateful to John Rust, Matthew Gentzkow, Connan Snider, Phillip Leslie, Yossi Spiegel, and participants at the 2012 UCLA Alumni Conference, the Fifth Workshop on the Economics of Advertising and Marketing, and the Yale Marketing-Industrial Organization Conference for constructive comments. Financial support from the University of Maryland and the Sogang University Research Grant of 2011 (#201110038.01) is graciously acknowledged. All errors are ours. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
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. citation courtesy of