Risk-adjusted monitoring of online user-generated reviews via user preference learning
本文提出一种风险调整控制图，通过联合潜在因子模型学习用户评分偏差和偏好，从评论中分离个人风险因素以准确检测产品性能异常，实验表明该方法在评论偏移检测和异常解释方面表现优异。
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’s rating bias and latent preference, ignoring customer’s latent factors may lead to misjudgements about online product performance. Therefore, this study proposes a risk-adjusted control chart for monitoring the decrease of rating scores in reviews, 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 study on beer review corpus and extensive simulation studies, the proposed method shows superior performance in review shift detection, also with good diagnostic performance for explaining the reasons behind anomalies.