Online ratings influence customer decision-making, yet standard aggregation methods, such as the sample mean, fail to adapt to quality changes over time and ignore review heterogeneity (e.g., review sentiment, a review's helpfulness). To address these challenges, we demonstrate the value of using the Gaussian process (GP) framework for rating aggregation. Specifically, we present a tailored GP model that captures the dynamics of ratings over time while additionally accounting for review heterogeneity. Based on 121,123 ratings from Yelp, we compare the predictive power of different rating aggregation methods in predicting future ratings, thereby finding that the GP model is considerably more accurate and reduces the mean absolute error by 10.2% compared to the sample mean. Our findings have important implications for marketing practitioners and customers. By moving beyond means, designers of online reputation systems can display more informative and adaptive aggregated rating scores that are accurate signals of expected customer satisfaction.

翻译：在线评分影响客户决策，但标准聚合方法（如样本均值）无法适应随时间变化的质量波动，且忽略了评论的异质性（例如评论情感、评论的有用性）。为应对这些挑战，我们论证了使用高斯过程（GP）框架进行评分聚合的价值。具体而言，我们提出了一种定制化的GP模型，该模型既能捕捉评分随时间变化的动态特性，又能同时考虑评论的异质性。基于来自Yelp的121,123条评分数据，我们比较了不同评分聚合方法在预测未来评分时的预测能力，结果发现GP模型显著更准确，与样本均值相比，平均绝对误差降低了10.2%。我们的研究对市场营销从业者和客户具有重要启示。通过超越均值方法，在线声誉系统的设计者可以展示更具信息性和适应性的聚合评分，这些评分能更准确地反映预期的客户满意度。