Özet:

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.

Anahtar Kelimeler: