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Mathematical Problems in Engineering
Volume 2017, Article ID 1383891, 12 pages
Research Article

SHMF: Interest Prediction Model with Social Hub Matrix Factorization

1Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, China
2University of Chinese Academy of Sciences, Beijing 100049, China
3Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230088, China
4University of Science and Technology of China, Hefei, Anhui 230031, China

Correspondence should be addressed to Shu Yan;

Received 24 January 2017; Accepted 5 June 2017; Published 22 August 2017

Academic Editor: Zonghua Zhang

Copyright © 2017 Chaoyuan Cui 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.


With the development of social networks, microblog has become the major social communication tool. There is a lot of valuable information such as personal preference, public opinion, and marketing in microblog. Consequently, research on user interest prediction in microblog has a positive practical significance. In fact, how to extract information associated with user interest orientation from the constantly updated blog posts is not so easy. Existing prediction approaches based on probabilistic factor analysis use blog posts published by user to predict user interest. However, these methods are not very effective for the users who post less but browse more. In this paper, we propose a new prediction model, which is called SHMF, using social hub matrix factorization. SHMF constructs the interest prediction model by combining the information of blogs posts published by both user and direct neighbors in user’s social hub. Our proposed model predicts user interest by integrating user’s historical behavior and temporal factor as well as user’s friendships, thus achieving accurate forecasts of user’s future interests. The experimental results on Sina Weibo show the efficiency and effectiveness of our proposed model.