SHMF: Interest Prediction Model with Social Hub Matrix Factorization

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...

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Bibliographic Details
Published in:Mathematical problems in engineering 2017-01, Vol.2017 (2017), p.1-12
Main Authors: Gao, Sen, Wu, Yun, Wang, Hongze, Cui, Chaoyuan, Yan, Shu
Format: Article
Language:English
Subjects:
Blogs
Collaboration
Datasets
Experiments
Factor analysis
Factorization
Friendship
Information systems
Knowledge management
Mathematical models
Mathematical problems
Recommender systems
Science
Social networks
Socialization
Software
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2017/1383891