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dc.contributor.authorDai, Weijia
dc.contributor.authorJin, Ginger
dc.contributor.authorLee, Jungmin
dc.contributor.authorLuca, Michael
dc.date.accessioned2019-08-28T11:34:57Z
dc.date.issued2018-09
dc.identifier.citationDai, 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.en_US
dc.identifier.issn1570-7156en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:41264476*
dc.description.abstractBecause 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.en_US
dc.language.isoen_USen_US
dc.publisherSpringer Natureen_US
dc.relation.isversionofhttp://rdcu.be/FFJlen_US
dc.relation.hasversionhttp://www.nber.org/papers/w18567en_US
dash.licenseMETA_ONLY
dc.subjectsocial and collaborative networksen_US
dc.subjectinformationen_US
dc.subjectinterneten_US
dc.subjectmathematical methodsen_US
dc.titleAggregation of consumer ratings: an application to Yelp.comen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalQuantitative Marketing and Economicsen_US
dash.depositing.authorLuca, Michael
dc.date.available2019-08-28T11:34:57Z
dc.identifier.doi10.1007/s11129-017-9194-9
dc.source.journalQuant Mark Econ
dash.source.volume16;3
dash.source.page289-339


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