Exploiting social context for review quality prediction 论文

摘要

Online reviews in which users publish detailed commentary about their experiences and opinions with products, services, or events are extremely valuable to users who rely on them to make informed decisions. However, reviews vary greatly in quality and are con-stantly increasing in number, therefore, automatic assessment of review helpfulness is of growing importance. Previous work has addressed the problem by treating a review as a stand-alone docu-ment, extracting features from the review text, and learning a func-tion based on these features for predicting the review quality. In this work, we exploit contextual information about authors ’ iden-tities and social networks for improving review quality prediction. We propose a generic framework for incorporating social context information by adding regularization constraints to the text-based predictor. Our approach can effectively use the social context infor-mation available for large quantities of unlabeled reviews. It also has the advantage that the resulting predictor is usable even when social context is unavailable. We validate our framework within a real commerce portal and experimentally demonstrate that using social context information can help improve the accuracy of re-view quality prediction especially when the available training data is sparse.