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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 936397, 10 pages
http://dx.doi.org/10.1155/2015/936397
Research Article

A Multidimensional Nonnegative Matrix Factorization Model for Retweeting Behavior Prediction

Mengmeng Wang,1,2 Wanli Zuo,1,2 and Ying Wang1,2,3

1College of Computer Science and Technology, Jilin University, Changchun 130012, China
2Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Ministry of Education, Changchun 130012, China
3College of Mathematics, Jilin University, Changchun 130012, China

Received 22 October 2014; Accepted 6 February 2015

Academic Editor: Sergio Preidikman

Copyright © 2015 Mengmeng Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Today microblogging has increasingly become a means of information diffusion via user’s retweeting behavior. As a consequence, exploring on retweeting behavior is a better way to understand microblog’s transmissibility in the network. Hence, targeted at online microblogging, a directed social network, along with user-based features, this paper first built content-based features, which consisted of URL, hashtag, emotion difference, and interest similarity, based on time series of text information that user posts. And then we measure relationship-based factor in social network according to frequency of interactions and network structure which blend with temporal information. Finally, we utilize nonnegative matrix factorization to predict user’s retweeting behavior from user-based dimension and content-based dimension, respectively, by employing strength of social relationship to constrain objective function. The results suggest that our proposed method effectively increases retweeting behavior prediction accuracy and provides a new train of thought for retweeting behavior prediction in dynamic social networks.