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Combining activity-evaluation information with NMF for trust-link prediction in social media

Citation

Matsutani, K and Kumano, M and Kimura, M and Saito, K and Ohara, K and Motoda, H, Combining activity-evaluation information with NMF for trust-link prediction in social media, Proceedings of the 2015 IEEE International Conference on Big Data, 29 October - 01 November 2015, Santa Clara, CA, USA, pp. 2263-2272. ISBN 9781479999255 (2015) [Refereed Conference Paper]


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Copyright Statement

Copyright 2015 IEEE

Official URL: http://cci.drexel.edu/bigdata/bigdata2015/

DOI: doi:10.1109/BigData.2015.7364015

Abstract

Acquiring a network of trust relations among users in social media sites, e.g., item-review sites, is important for analyzing users' behavior and efficiently finding reliable information on the Web. We address the problem of predicting trustlinks among users for an item-review site. Non-negative matrix factorization (NMF) methods have recently been shown useful for trust-link prediction in such a site where both link and activity information is available. Here, a user activity in an item-review site means posting a review and giving a rating for an item. In this paper, for better trust-link prediction, we propose a new NMF method that incorporates people's evaluation of users' activities as well as trust-links and users' activities themselves. We further apply it to an analysis of users' behavior. Using two real world item-review sites, we experimentally demonstrate the effectiveness of the proposed method.

Item Details

Item Type:Refereed Conference Paper
Keywords:social media mining, trust-link prediction, behavioral analysis, non-negative matrix factorization
Research Division:Information and Computing Sciences
Research Group:Library and Information Studies
Research Field:Social and Community Informatics
Objective Division:Information and Communication Services
Objective Group:Information Services
Objective Field:Information Services not elsewhere classified
Author:Motoda, H (Dr Hiroshi Motoda)
ID Code:109997
Year Published:2015
Deposited By:Computing and Information Systems
Deposited On:2016-07-11
Last Modified:2016-08-09
Downloads:0

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