CHARACTERIZING AND PREDICTING EARLY REVIEWERS FOR EFFECTIVE PRODUCT MARKETING ON ECOMMERCE WEBSITES

Abstract

Online reviews have become an important source of instruction for users
before manufacture an informed procure decision. Early reviews of a product tend to have a
high effect on the ensuing 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 ecommerce platforms, i.e., Amazon and Yelp. In specific, we divide product lifetime
into three uninterrupted phase, namely early, majority and straggler. A user who has posted a
review in the early stage is contemplating as an untimely observer. 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 observer tends to assign a higher average rating score; and (2) an early observer
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 present
a novel margin-based embedding model for early reviewer divination. Extensive experiments
on two different ecommerce datasets have shown that our proposed approach outperforms a
number of aggressive baselines.