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ZTE Communications ›› 2014, Vol. 12 ›› Issue (1): 57-61.DOI: 10.3939/j.issn.1673-5188.2014.01.009

• • 上一篇    

A User-Recommendation Method Based on Social Media

Hong Chen1, Shengmei Luo1, Lei Hu1, and Xiuwen Wang2   

  1. 1. ZTE Corporation, Nanjing 210012, China;
    2. National Computer network Emergency Response technical Team,Beijing100029, China
  • 收稿日期:2013-08-20 出版日期:2014-03-25 发布日期:2014-03-25
  • 作者简介:Hong Chen (chen.hong3@zte.com.cn) received her BS degree from the Department of Information Engineering, Nangjing University of Posts and Telecommunications, in 2007. She currently works as a senior research engineer at ZTE Corporation. Her research interests incloud social network analysis and Intelligent Question Answering. She holds four patents .
    Shengmei Luo (luo.shengmei@zte.com.cn) received his MS degree in telecommunication and electronics engineering from Harbin Institue of Technology, China, in 1996. He is a chief architect at ZTE Corporation. His research interests include cloud mobile Internet, cloud computing, and industrial application of planning. He has published 20 papers.
    Lei Hu (hu.lei2@zte.com.cn) received his MS degree from the Laboratory of Intelligent Recognition and Image Processing, Beihang University, in 2008. He is a senior research engineer in the area of mass data analysis at ZTE Corporation. His research interests include data mining, information retrieval, and social network analysis. He holds three patents and has published four papers in these fields.
    Xiuwen Wang (wxw@cert.org.cn) received her PhD degree in computer system architecture from the Institute of Computing Technology, Chinese Academy of Sciences, in 2010. She now works as a senior research engineer in the China National Computer Network Emergency Response Team (CNCERT). Her research interests include cloud social network analysis and data analysis.

A User-Recommendation Method Based on Social Media

Hong Chen1, Shengmei Luo1, Lei Hu1, and Xiuwen Wang2   

  1. 1. ZTE Corporation, Nanjing 210012, China;
    2. National Computer network Emergency Response technical Team,Beijing100029, China
  • Received:2013-08-20 Online:2014-03-25 Published:2014-03-25
  • About author:Hong Chen (chen.hong3@zte.com.cn) received her BS degree from the Department of Information Engineering, Nangjing University of Posts and Telecommunications, in 2007. She currently works as a senior research engineer at ZTE Corporation. Her research interests incloud social network analysis and Intelligent Question Answering. She holds four patents .
    Shengmei Luo (luo.shengmei@zte.com.cn) received his MS degree in telecommunication and electronics engineering from Harbin Institue of Technology, China, in 1996. He is a chief architect at ZTE Corporation. His research interests include cloud mobile Internet, cloud computing, and industrial application of planning. He has published 20 papers.
    Lei Hu (hu.lei2@zte.com.cn) received his MS degree from the Laboratory of Intelligent Recognition and Image Processing, Beihang University, in 2008. He is a senior research engineer in the area of mass data analysis at ZTE Corporation. His research interests include data mining, information retrieval, and social network analysis. He holds three patents and has published four papers in these fields.
    Xiuwen Wang (wxw@cert.org.cn) received her PhD degree in computer system architecture from the Institute of Computing Technology, Chinese Academy of Sciences, in 2010. She now works as a senior research engineer in the China National Computer Network Emergency Response Team (CNCERT). Her research interests include cloud social network analysis and data analysis.

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摘要： User - analysis techniques are mainly used to recommend friends and information. This paper discusses the data characteristics of microblog users and describes a multidimensional user recommendation algorithm that takes into account microblog length, relativity between microblog and users, and familiarity between users. The experimental results show that this multidimensional algorithm is more accurate than a traditional recommendation algorithm.

关键词: social media, user recommendation, information recommendation, relation analysis

Abstract: User - analysis techniques are mainly used to recommend friends and information. This paper discusses the data characteristics of microblog users and describes a multidimensional user recommendation algorithm that takes into account microblog length, relativity between microblog and users, and familiarity between users. The experimental results show that this multidimensional algorithm is more accurate than a traditional recommendation algorithm.

Key words: social media, user recommendation, information recommendation, relation analysis