A novel approach for recommending ranked user-generated reviews

Abstract

User-generated reviews play an important role for potential consumers in making purchase decisions. However, the quality and helpfulness of user-generated reviews are unavailable unless consumers read through them. Existing helpfulness assessing models make use of the positive vote fraction as a benchmark. This benchmark methodology ignores the voter population size and the uncertainty of the helpfulness estimation. In this paper, we propose a user-generated review recommendation model based on the probability density of the review's helpfulness. Our experimental results confirm that our approach can effectively assess the helpfulness of user-generated reviews and recommend the most helpful ones to consumers. © 2010 Springer-Verlag Berlin Heidelberg.