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2008 Technical Reports

Reviews Are Not Equally Important: Predicting the Helpfulness of Online Reviews

Yang Liu, Xiangji Huang, Aijun An and Xiaohui Yu

Technical Report CSE-2008-05

York University

July 12, 2008


Online reviews provide a valuable resource for potential customers to make purchase decisions. However, thesheer volume of available reviews as well as the large variations in the review quality present a big impediment tothe effective use of the reviews, as the most helpful reviews may be buried in the large amount of reviews of lowqualities. The goal of this paper is to develop models and algorithms for predicting the helpfulness of reviews, whichprovides the basis for discovering the most helpful reviews for given products. We first show that the helpfulnessof a review depends on three important factors: the reviewer.s expertise, the writing style of the review, and thetimeliness of the review. Based on the analysis of those factors, we present HelpMeter, a nonlinear regressionmodel for helpfulness prediction. Our empirical study on the IMDB movie reviews dataset demonstrates that theproposed approach is highly effective.

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