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Characterizing and predicting early reviewers for effective product marketing on e-commerce websites

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Bai, Ting, Zhao, Xin, He, Yulan, Nie, Jian-Yun and Wen, Ji-Rong (2018) Characterizing and predicting early reviewers for effective product marketing on e-commerce websites. IEEE Transactions on Knowledge and Data Engineering, 30 (12). pp. 2271-2284. doi:10.1109/TKDE.2018.2821671

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Official URL: http://dx.doi.org/10.1109/TKDE.2018.2821671

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Abstract

Online reviews have become an important source of information for users before making an informed purchase decision. Early reviews of a product tend to have a high impact on the subsequent product sales. In this paper, we take the initiative to study the behavior characteristics of early reviewers through their posted reviews on two real-world large e-commerce platforms, i.e., Amazon and Yelp. In specific, we divide product lifetime into three consecutive stages, namely early, majority and laggards. A user who has posted a review in the early stage is considered as an early reviewer. We quantitatively characterize early reviewers based on their rating behaviors, the helpfulness scores received from others and the correlation of their reviews with product popularity. We have found that (1) an early reviewer tends to assign a higher average rating score; and (2) an early reviewer tends to post more helpful reviews. Our analysis of product reviews also indicates that early reviewers' ratings and their received helpfulness scores are likely to influence product popularity. By viewing review posting process as a multiplayer competition game, we propose a novel margin-based embedding model for early reviewer prediction. Extensive experiments on two different e-commerce datasets have shown that our proposed approach outperforms a number of competitive baselines.

Item Type: Journal Article
Subjects: H Social Sciences > HF Commerce
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): Electronic commerce, Consumers -- Attitudes, Amazon.com (Firm) -- Case studies, Yelp -- Case studies
Journal or Publication Title: IEEE Transactions on Knowledge and Data Engineering
Publisher: IEEE Computer Society
ISSN: 1041-4347
Official Date: 1 December 2018
Dates:
DateEvent
1 December 2018Published
2 April 2018Available
19 March 2018Accepted
Volume: 30
Number: 12
Page Range: pp. 2271-2284
DOI: 10.1109/TKDE.2018.2821671
Status: Peer Reviewed
Publication Status: Published
Publisher Statement: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
1502502[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
2014CB340403 [MSTPRC] Ministry of Science and Technology of the People's Republic of Chinahttp://dx.doi.org/10.13039/501100002855
4162032Natural Science Foundation of Beijing Municipalityhttp://dx.doi.org/10.13039/501100004826
UNSPECIFIEDRenmin University of Chinahttp://dx.doi.org/10.13039/501100004260

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