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.
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Zhang, R., Tran, T.T. (2010). A Novel Approach for Recommending Ranked User-Generated Reviews. In: Farzindar, A., Kešelj, V. (eds) Advances in Artificial Intelligence. Canadian AI 2010. Lecture Notes in Computer Science(), vol 6085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13059-5_38
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DOI: https://doi.org/10.1007/978-3-642-13059-5_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13058-8
Online ISBN: 978-3-642-13059-5
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