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Beyond Means: A Dynamic Framework for Predicting Customer Satisfaction
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DOI：10.1016/j.ijresmar.2026.03.006.png)
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Abstract
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• Standard aggregation methods for online ratings fail to adapt to review heterogeneity • We develop a framework based on a tailored Gaussian process that captures the heterogeneous dynamics of ratings • Predictive power improves with 10.2 • We use 121,123 Yelp reviews to train and evaluate our model • Our model can be used within online rating systems to better reflect customer satisfaction
Keywords:
Gaussian process
Bayesian modeling
reputation systems
online ratings
time-series predictions
machine learning
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