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SHMF: Interest Prediction Model with Social Hub Matrix Factorization

文献类型：期刊论文

作者Cui, Chaoyuan1; Wang, Hongze2; Wu, Yun3; Gao, Sen4; Yan, Shu1
刊名MATHEMATICAL PROBLEMS IN ENGINEERING
出版日期2017
DOI10.1155/2017/1383891
文献子类Article
英文摘要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 SinaWeibo show the efficiency and effectiveness of our proposed model.
WOS研究方向Engineering ; Mathematics
语种英语
WOS记录号WOS:000408219800001
资助机构National Natural Science Foundation of China(31371340) ; National Natural Science Foundation of China(31371340) ; National Natural Science Foundation of China(31371340) ; National Natural Science Foundation of China(31371340) ; National Natural Science Foundation of China(31371340) ; National Natural Science Foundation of China(31371340) ; National Natural Science Foundation of China(31371340) ; National Natural Science Foundation of China(31371340) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Natural Science Foundation of China(31371340) ; National Natural Science Foundation of China(31371340) ; National Natural Science Foundation of China(31371340) ; National Natural Science Foundation of China(31371340) ; National Natural Science Foundation of China(31371340) ; National Natural Science Foundation of China(31371340) ; National Natural Science Foundation of China(31371340) ; National Natural Science Foundation of China(31371340) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604) ; National Key Technologies Research and Development Program of China(2016YFB0502604)
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/33643]  
专题合肥物质科学研究院_中科院合肥智能机械研究所
作者单位1.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Hefei 230031, Anhui, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Appl Technol, Hefei 230088, Anhui, Peoples R China
4.Univ Sci & Technol China, Hefei 230031, Anhui, Peoples R China
推荐引用方式
GB/T 7714		 Cui, Chaoyuan,Wang, Hongze,Wu, Yun,et al. SHMF: Interest Prediction Model with Social Hub Matrix Factorization[J]. MATHEMATICAL PROBLEMS IN ENGINEERING,2017.
APA Cui, Chaoyuan,Wang, Hongze,Wu, Yun,Gao, Sen,&Yan, Shu.(2017).SHMF: Interest Prediction Model with Social Hub Matrix Factorization.MATHEMATICAL PROBLEMS IN ENGINEERING.
MLA Cui, Chaoyuan,et al."SHMF: Interest Prediction Model with Social Hub Matrix Factorization".MATHEMATICAL PROBLEMS IN ENGINEERING (2017).

入库方式： OAI收割

来源：合肥物质科学研究院

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