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作者单位:Social Computing, Behavioral-Cultural Modeling, and Prediction
丛书名:978-3-319-16267-6
刊物类别:Computer Science
刊物主题:Artificial Intelligence and Robotics Computer Communication Networks Software Engineering Data Encryption Database Management Computation by Abstract Devices Algorithm Analysis and Problem Complexity
出版者:Springer Berlin / Heidelberg
ISSN:1611-3349
文摘
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 Page Rank 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.