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The Scientific World Journal
Volume 2014 (2014), Article ID 420841, 10 pages
http://dx.doi.org/10.1155/2014/420841
Research Article

Followee Recommendation in Microblog Using Matrix Factorization Model with Structural Regularization

Yan Yu1,2 and Robin G. Qiu1,3

1College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2Computer Science Department, Southeast University Chengxian College, Nanjing 210088, China
3Information Science Department, Pennsylvania State University, Great Valley, Malvern, PA 16802, USA

Received 12 January 2014; Accepted 11 February 2014; Published 31 March 2014

Academic Editors: Y. Blanco Fernandez and D. Tao

Copyright © 2014 Yan Yu and Robin G. Qiu. 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

Microblog that provides us a new communication and information sharing platform has been growing exponentially since it emerged just a few years ago. To microblog users, recommending followees who can serve as high quality information sources is a competitive service. To address this problem, in this paper we propose a matrix factorization model with structural regularization to improve the accuracy of followee recommendation in microblog. More specifically, we adapt the matrix factorization model in traditional item recommender systems to followee recommendation in microblog and use structural regularization to exploit structure information of social network to constrain matrix factorization model. The experimental analysis on a real-world dataset shows that our proposed model is promising.