Exploiting shopping and reviewing behavior to re-score online evaluations

Rong Zhang, Chaofeng Sha, Minqi Zhou, Aoying Zhou

Abstract: Analysis to product reviews has attracted great attention from both academia and industry. Generally the evaluation scores of reviews are used to generate the average scores of products and shops for future potential users. However, in the real world, there is the inconsistency problem between the evaluation scores and review content, and some customers do not give out fair reviews. In this work, we focus on detecting the credibility of customers by analyzing online shopping and review behaviors, and then we re-score the reviews for products and shops. In the end, we evaluate our algorithm based on the real data set from Taobao, the biggest E-commerce site in China.