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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
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 | |||||||||||||||
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| 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: |
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| 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: |
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