{"title":"Personalized Ranking of Online Reviews Based on Consumer Preferences in Product Features","authors":"Anupama Dash, Dongsong Zhang, Lina Zhou","doi":"10.1080/10864415.2021.1846852","DOIUrl":null,"url":null,"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.","PeriodicalId":13928,"journal":{"name":"International Journal of Electronic Commerce","volume":"25 1","pages":"29 - 50"},"PeriodicalIF":4.2000,"publicationDate":"2021-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10864415.2021.1846852","citationCount":"22","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Electronic Commerce","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1080/10864415.2021.1846852","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 22
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.
期刊介绍:
The International Journal of Electronic Commerce is the leading refereed quarterly devoted to advancing the understanding and practice of electronic commerce. It serves the needs of researchers as well as practitioners and executives involved in electronic commerce. The Journal aims to offer an integrated view of the field by presenting approaches of multiple disciplines.
Electronic commerce is the sharing of business information, maintaining business relationships, and conducting business transactions by digital means over telecommunications networks. The Journal accepts empirical and interpretive submissions that make a significant novel contribution to this field.