Probabilistic Modeling of User-Generated Reviews

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. Automatically predicting the helpfulness of user-generated reviews can assist consumers in discovering helpful reviews. Existing helpfulness assessing models make use of the positive vote fraction as a benchmark and focus on heuristically finding a ``best guess´´ value, which is a point estimate of helpfulness. 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, rather than using the positive vote fraction. Our proposed model exploits probabilistic methodology to infer the helpfulness distribution. Furthermore, our experimental results confirm that our approach can effectively assess the helpfulness of user-generated reviews and recommend the most helpful ones to consumers.