Aggregation of Consumer Ratings: An Application to Yelp.com
41 Pages Posted: 5 Nov 2014 Last revised: 13 Nov 2017
Date Written: November 11, 2017
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.
JEL Classification: D08, L15, L86
Suggested Citation: Suggested Citation