A Novel Hybrid Friends Recommendation Framework for Twitter

  • Yan Zhao
  • Jia ZhuEmail author
  • Mengdi Jia
  • Wenyan Yang
  • Kai Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10367)


As one of the key features of social networks, friends recommendation is a kind of link prediction task with ranking that was extensively investigated recently in the area of social networks analysis as users would like to follow people who have similar interests to them. We use Twitter as a case study and propose a novel hybrid friends recommendation framework that is not only based on friends relationship but also users’ location information, which are recorded by Twitter when they posted their tweets. Our framework can recommend friends to users who have similar interests based on location features by using collaborative filtering to effectively filter out those common places which are meaningless, e.g., bus station; and focuses on those places that have high probability that people are there more likely to become friends, e.g., dance studio. In addition, we propose a multiple classifiers combination method to leverage the information contained in friends and locations features in order to get better outcomes. We evaluate our framework on two real corpora from Twitter, and the favorable results indicate that our proposed approach is feasible.


Social network Recommendation systems 



This work was supported by Natural Science Foundation of Guangdong Province, China (No. 2015A030310509), and the S&T Projects of Guangdong Province (No. 2016A030303055, No. 2016B030305004, 2016B010109008), Natural Science Foundation of China (No. 61532018 and No. 61502324).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yan Zhao
    • 1
  • Jia Zhu
    • 2
    Email author
  • Mengdi Jia
    • 1
  • Wenyan Yang
    • 1
  • Kai Zheng
    • 1
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.School of Computer ScienceSouth China Normal UniversityGuangzhouChina

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