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.

Download paper in PDF format.

The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a noncommercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.