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On the Volatility of Online Ratings:An Empirical Study

机译：关于在线评级的波动性：实证研究

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摘要

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：许多在线评级系统代表了使用度量等度量和评级分配等度量的产品质量。然而，随着评论积累的评论，平均值通常变得稳定，因此它没有反映了最新用户评级的趋势。此外，了解趋势的任何变化是否真正重大，需要占产品评级历史的波动性。开发更好的评级聚集技术应专注于量化评级中的波动性，以适当的重量或折扣较旧的评级。我们提出了一个基于股票市场度量的理论模型，称为平均评级波动（ARV）。这捕获了这些评级中存在的波动。接下来，ARV被映射到加权（老化）过去额定值的折扣因子，并用作棕色简单指数平滑的系数，以产生总均值等级。这种提出的方​​法代表了产品的“真实”质量，因为它占产品评级历史的波动和趋势。使用来自亚马逊数据的多种产品类别的评级波动性的经验结果进一步激励所提出的方法的需求和适用性。

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