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

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.

KEY WORDS AND PHRASES:

• Consumer preferences
• EwoM
• latent class regression
• online consumer reviews
• personalization
• product-feature based ranking

Additional information

Notes on contributors

Anupam Dash

ANUPAM DASH ([email protected]) is a research scientist in the Distributed Intelligent System and Advanced Computing Group at Intelligent Automation. She received her Ph.D. from University of Maryland, Baltimore County. Her research interests include social media analytics, advanced statistical data modeling, machine learning, natural language processing, and sentiment analysis.

Dongsong Zhang

DONGSONG ZHANG ([email protected]; corresponding author) is a Belk Endowed Chair Professor in Business Analytics in the Department of Business Information Systems and Operations Management at the University of North Carolina at Charlotte. He received his Ph.D. in Management Information Systems from the University of Arizona. Dr. Zhang has published 160 research papers in the domains of social media analytics, health information technology, mobile human–computer interaction, and intelligent decision making. His work has appeared in MIS Quarterly, Journal of Management Information Systems, ACM Transactions, IEEE Transactions, Information & Management, and Decision Support Systems, among others. He has received research funding and awards from National Science Foundation, National Institute of Health, and Google.

Lina Zhou

LINA ZHOU ([email protected]) is a full professor in the Department of Business Information Systems and Operations Management in the Belk College of Business at the University of North Carolina at Charlotte. Her research interests span the areas of social media analytics, deception detection, biomedical informatics, and intelligent mobile interface. She has (co-)authored articles published in journals such as MIS Quarterly, Journal of Management Information Systems, Communications of the ACM, Information & Management, and Decision Support Systems.