OPTIMAL AGGREGATION OF CONSUMER RATINGS: AN APPLICATION TO YELP.COM
OPTIMAL AGGREGATION OF CONSUMER RATINGS: AN APPLICATION TO YELP.COM
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发表时间：
2014
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通讯作者：
Weijia Dai;Ginger Z. Jin;Jungmin Lee;Michael Luca;John Rust;Matthew Gentzkow;Connan Snider;Phillip Leslie;Yossi Spiegel
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作者：
Weijia Dai;Ginger Z. Jin;Jungmin Lee;Michael Luca;John Rust;Matthew Gentzkow;Connan Snider;Phillip Leslie;Yossi Spiegel
消费者评论网站利用大众的智慧，每个产品都被多次评论（有些评论超过1000条）。正因为如此，信息聚合的方式是消费者评论网站面临的一个核心决策。给定一组评论，构建平均评级的最佳方法是什么？我们提供了一种结构化的方法来回答这个问题，允许(1)审稿人在严格性和准确性上有所不同，(2)审稿人受到现有审稿人的影响，以及(3)产品质量随时间而变化。将这种方法应用于Yelp.com上的餐馆评论，我们为所有餐馆（cid:3）构建最佳评级，并将它们与Yelp显示的算术平均值进行比较。根据我们如何解释餐馆内评论的下降趋势，我们发现19.1-41.38%的简单平均评级与最佳评级相差超过0.15颗星，5.33-19.1%的样本周期结束时相差超过0.25颗星。此外，随着时间的推移，随着餐厅评论的积累，这种偏差会显著增加。这表明，通过实施最优评级可以获得巨大收益，尤其是随着Yelp的增长。我们的算法可以灵活地应用于许多不同的评论设置。
Consumer review websites leverage the wisdom of the crowd, with each product being reviewed many times (some with more than 1,000 reviews). Because of this, the way in which information is aggregated is a central decision faced by consumer review websites. Given a set of reviews, what is the optimal way to construct an average rating? We offer a structural approach to answering this question, 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 approach to restaurant reviews from Yelp.com, we construct optimal ratings for all restaurants (cid:3) and compare them to the arithmetic averages displayed by Yelp. Depending on how we interpret the downward trend of reviews within a restaurant, we find 19.1-41.38% of the simple average ratings are more than 0.15 stars away from optimal ratings, and 5.33-19.1% are more than 0.25 stars away at the end of our sample period. Moreover, the deviation grows significantly as a restaurant accumulates reviews over time. This suggests that large gains could be made by implementing optimal ratings, especially as Yelp grows. Our algorithm can be flexibly applied to many different review settings.