A novel approach for recommending ranked user-generated reviews

Authors:
Richong Zhang;Thomas T. Tran
Affiliations:
School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canada;School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canada
Venue:
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Year:
2010

Citing 3
Cited 1

Modeling and Predicting the Helpfulness of Online Reviews

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Automatically assessing review helpfulness

EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

Identifying helpful online reviews: A product designer's perspective

Computer-Aided Design

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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.