Efficient learning of user conformity on review score

We propose a simple and efficient method that learns and assesses the conformity of each user of an online review system from the observed review score record. The model we use is a modified Voter model that takes account of the conformity of each user. Conformity is learnable quite efficiently with a few tens of iterations by maximizing the log-likelihood given the observed data. The proposed method was evaluated and confirmed effective by two review datasets. It could identify both high and low conformity users. Users with high conformity are not necessarily early adopters. Their scores are influential to drive the consensus score. The user ranking of conformity was compared with PageRank and HITS in which user network was roughly approximated by the directed graph induced by the observed data. The proposed method gives more interpretable ranking, and the global property of high conformity users was identified.