Aggregation of Consumer Ratings: An Application to Yelp.com / Weijia Dai, Ginger Z. Jin, Jungmin Lee, Michael Luca.

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Dai, Weijia

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Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w18567.Publication details: Cambridge, Mass. National Bureau of Economic Research 2012.Description: 1 online resource: illustrations (black and white)Subject(s):

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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.

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Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w18567 (Browse shelf(Opens below))	Not For Loan

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November 2012.

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.