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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 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
UTAS Author:	Motoda, H (Dr Hiroshi Motoda)
ID Code:	109997
Year Published:	2015
Deposited By:	Information and Communication Technology
Deposited On:	2016-07-11
Last Modified:	2017-11-13
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