Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites

Ting Bai, Xin Zhao, Yulan He, Jian-yun Nie, Ji-rong Wen

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)2271-2284
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number12
Early online date2 Apr 2018
Publication statusPublished - 1 Dec 2018

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