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 behaviour 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 uninterrupted phase and quantitatively characterize early
reviewers based on their rating behaviours, the helpfulness scores received from others and the correlation of their reviews with
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 e-commerce datasets have shown that our
proposed approach outperforms a number of aggressive baselines.
