Aggregation of consumer ratings: an application to Yelp.com |
| |
| Authors: | Weijia??Dai Ginger?Jin Jungmin?Lee Michael?Luca |
| |
| Institution: | 1.Lehigh University,Bethlehem,USA;2.University of Maryland & NBER,College Park,USA;3.Seoul National University,Seoul,South Korea;4.Harvard Business School,Boston,USA |
| |
| 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. |
| |
| Keywords: | |
| 本文献已被 SpringerLink 等数据库收录！ |
|
