Abstract
ABSTRACT Online consumer reviews (OCRs) can function as a venue for digital collaboration among various stakeholders to better meet collaborate in consumer needs. The sheer volume of OCRs, however, has posed challenges to efficient search and navigation. Importantly, consumers' needs of product information may differ because of their different preferences in product features. Such differences remain underaddressed in the OCR literature. This research introduces a novel framework - Product feature based Personalized Review Ranking (P2R2), which predicts review helpfulness for individual consumers based on their preferences in product features using a latent class regression model. The framework also leverages the similarities among different consumers to derive consumer classes. An empirical evaluation of a prototype of P2R2 with a user study provides strong evidence that the review rankings produced by P2R2 are more similar to users’ self-rankings than by a helpfulness vote based ranking method. The findings of this study offer theoretical insights, novel technical design artifacts, and empirical evidence for enhancing OCR platforms with review ranking personalization.
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