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Efficient learning of user conformity on review score

Citation

Saito, K and Ohara, K and Kimura, M and Motoda, H, Efficient learning of user conformity on review score, Social Computing, Behavioral-Cultural Modeling, and Prediction: 8th International Conference, SBP 2015, 31 March - 3 April, Washington, DC, USA, pp. 182-192. ISBN 9783319162676 (2015) [Refereed Conference Paper]


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Copyright Statement

Copyright 2015 Springer International Publishing

Official URL: http://doi.org.10.1007/978-3-319-16268-3 19

DOI: doi:10.1007/978-3-319-16268-3_19

Abstract

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.

Item Details

Item Type:Refereed Conference Paper
Keywords:social media, conformity, review score, learning
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Artificial Intelligence and Image Processing not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Computer Software and Services not elsewhere classified
Author:Motoda, H (Dr Hiroshi Motoda)
ID Code:106723
Year Published:2015
Deposited By:Computing and Information Systems
Deposited On:2016-02-18
Last Modified:2017-11-20
Downloads:0

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