On the Volatility of Online Ratings:An Empirical Study

机译：在线收视率波动性的实证研究

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Many online rating systems represent product quality using metrics such as the mean and the distribution of ratings. However, the mean usually becomes stable as reviews accumulate, and consequently, it does not reflect the trend emerging from the latest user ratings. Additionally, understanding whether any variation in the trend is truly significant requires accounting for the volatility of the product's rating history. Developing better rating aggregation techniques should focus on quantifying the volatility in ratings to appropriately weight or discount older ratings. We present a theoretical model based on stock market metrics, known as the Average Rating Volatility (ARV). which captures the fluctuation present in these ratings. Next, ARV is mapped to the discounting factor for weighting (aging) past ratings and used as the coefficient in Brown's Simple Exponential Smoothing to produce an aggregate mean rating. This proposed method represents the "true" quality of a product more accurately because it accounts for both volatility and trend in the product's rating history. Empirical findings on rating volatility for several product categories using data from Amazon further motivate the need and applicability of the proposed methodology.

机译：许多在线评分系统使用评分的平均值和分布等指标来表示产品质量。但是，随着评论的累积，平均值通常会变得稳定，因此，它不能反映最新用户评分中出现的趋势。此外，要了解趋势中的任何变化是否确实非常重要，就必须考虑产品评级历史记录的波动性。开发更好的收视率汇总技术应集中于量化收视率的波动性，以适当地加权或折旧较旧的收视率。我们基于股票市场指标提出一种理论模型，称为平均评级波动率（ARV）。捕获这些额定值中存在的波动。接下来，将ARV映射到折现因子，以对过去的评分进行加权（老化），并在Brown的简单指数平滑法中用作系数以产生总平均评分。此提议的方法可以更准确地表示产品的“真实”质量，因为它考虑了产品评级历史中的波动性和趋势。使用亚马逊提供的数据对几种产品类别的波动率进行实证研究的结果进一步激发了所提出方法的需求和适用性。

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来源
《E-Life: web-enabled convergence of commerce, work, and social life》|2011年|p.77-86|共10页
会议地点 Shanghai(CN);Shanghai(CN)
作者
Christopher S. Leberknight; Soumya Sen; Mung Chiang;
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作者单位

Princeton University, Department of Electrical Engineering, Olden Street,Princeton, NJ 08544;

Princeton University, Department of Electrical Engineering, Olden Street,Princeton, NJ 08544;

Princeton University, Department of Electrical Engineering, Olden Street,Princeton, NJ 08544;

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会议组织
原文格式 PDF
正文语种 eng
中图分类电子贸易、网上贸易;
关键词
consumer confidence; e-commerce; decision support; online ratings; reputation systems;

机译：消费者信心；电子商务;决策支持;在线评级；信誉系统;

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On the Volatility of Online Ratings:An Empirical Study

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