document: Exploiting social context for review quality prediction 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 constantly 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 document , extracting features from the review text , and learning a function based on these features for predicting the review quality . In this work , we exploit contextual information about authors ' identities 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 information 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 review quality prediction especially when the available training data is sparse .
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