Review recommendation with graphical model and EM algorithm

Automatically assessing the quality and helpfulness of consumer reviews is more and more desirable with the evolutionary development of online review systems. Existing helpfulness assessment methodologies make use of the positive vote fraction as a benchmark and heuristically find a "best guess" to estimate the helpfulness of review documents. This benchmarking methodology ignores the voter population size and treats the the same positive vote fraction as the same helpfulness value. We propose a review recommendation approach that make use of the probability density of the review helpfulness as the benchmark and exploit graphical model and Expectation Maximization (EM) algorithm for the inference of review helpfulness. The experimental results demonstrate that the proposed approach is superior to existing approaches.