A1085
Title: Risk-adjusted monitoring of online user-generated reviews via user preference learning
Authors: Qiao Liang - Southwestern University of Finance and Economics (China) [presenting]
Abstract: Online customer reviews provide valuable information about product quality, and recently, some control chart-based schemes have been proposed to detect product performance anomalies from reviews. As a review outcome depends not only on inherent product quality but also on customer rating bias and latent preference, ignoring customers' latent factors may lead to misjudgments about online product performance. Therefore, a risk-adjusted control chart is proposed for monitoring the decrease in review rating scores by separating the personal risk factors of individual customers from the assignable causes with respect to online product performance. The proposed risk-adjustment model is fitted by a united latent factor model that learns user rating bias and preference factors by combining both review texts and corresponding ratings. According to the experimental results of a real-world case and extensive simulation studies, the proposed method shows superior performance in review shift detection, with good interpretation for explaining the reasons behind anomalies.
