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World Wide Web

, Volume 21, Issue 2, pp 515–536 | Cite as

User interest mining via tags and bidirectional interactions on Sina Weibo

  • Lu Deng
  • Yan Jia
  • Bin Zhou
  • Jiuming Huang
  • Yi Han
Article
  • 276 Downloads

Abstract

Sina Weibo, one of the biggest social services in China, provides users with opportunities to share information and express their personal views, leading an explosive growth of information. How to recommend the right information to the proper person among massive data has received considerable critical attention in recent years. One of the main obstacles is the access to user topic interests. In this paper, we proposed an algorithm based on tags and bidirectional interactions to mine user topic interests on Sina Weibo. The algorithm, formulated by user interaction graph, fully takes advantage of the discordance between user interactions. Forward spread and back spread are thus utilized to update tag spread weights. We also quantify the impact of these two spread by tuning parameters on three sub data sets. In order to prove the superiority of the algorithm, we compare our algorithm with famous methods on Sina Weibo. The result demonstrates that our new algorithm outperforms other methods both in precision rate and recall rate, with the ability of mining user interest effectively with respect to tags and bidirectional interactions.

Keywords

Tag spread Interactions Sina Weibo User topic interests 

Notes

Acknowledgements

The authors would thank their colleagues, past and present, who contributed to the research described in this paper. The work described in this paper is partially supported by National Key Fundamental Research and Development Program (No.2013CB329601, No.2013CB329602, No.2013CB329604) and National Natural Science Foundation of China (No.61502517, No.61372191, No.61572492), 863 Program of China (Grant No. 2012AA01A401, 2012AA01A402, 2012AA013002), Project funded by China Postdoctoral Science Foundation (2013 M542560, 2015 T81129)

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.College of ComputerNational University of Defense TechnologyChangshaChina
  2. 2.Institute of Information EngineeringChinese Academy of SciencesBeijingChina

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