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.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
Ting Bai, Wanye Xin
Zhao Member, IEEE, Yulan He Member, IEEE, Jian-Yun Nie Member, IEEE, Ji-Rong
Wen Member, IEEE, “Characterizing and Predicting Early Reviewers for Effective
Product Marketing on E-Commerce Websites”, IEEE Transactions on Knowledge and
Data Engineering, 2018.