Aggregation of consumer ratings: an application to Yelp.com

Dai, Weijia; Jin, Ginger; Lee, Jungmin; Luca, Michael

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Author

Dai, Weijia

Jin, Ginger

Lee, Jungmin

Luca, Michael

Published Version

https://doi.org/10.1007/s11129-017-9194-9

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Citation

Dai, Weijia, Ginger Jin, Jungmin Lee, and Michael Luca. "Aggregation of Consumer Ratings: An Application to Yelp.com." Quantitative Marketing and Economics 16, no. 3 (September 2018): 289–339.

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.