Personalized Ranking of Online Reviews Based on Consumer Preferences in Product Features

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 ((PR2)-R-2), 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 (PR2)-R-2 with a user study provides strong evidence that the review rankings produced by (PR2)-R-2 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.

Automated summary

Online consumer reviews can be a platform for digital collaboration, but the sheer volume of reviews poses challenges to efficient search and navigation. This research introduces a novel framework, PR2-R-2, which predicts review helpfulness based on individual consumers' preferences in product features. The empirical evaluation demonstrates that the review rankings produced by PR2-R-2 are more similar to users' self-rankings than other methods, offering insights for enhancing OCR platforms with personalized review rankings.