Social Network User Recommendation Method Based on Dynamic Influence

  • Xiaoquan Xiong
  • Mingxin ZhangEmail author
  • Jinlon Zheng
  • Yongjun Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11242)


The rapid development and wide application of Online Social Network (OSN) has produced a large amount of social data. How to effectively use these data to recommend interesting relationships to users is a hot topic in social network mining. At present, the user relationship recommendation algorithm relies on similarity, and the user’s influence is insufficiently considered. Aiming at this problem, this paper proposes a new user influence evaluation model, and on this basis, a new user relationship recommendation algorithm (SIPMF) is proposed by combining similarity and dynamic influence. 2522366 Sina Weibo data were crawled to build an experimental data set for experiment. Compared with the typical relational recommendation algorithms SoRec, PMF, and FOF, the SIPMF algorithm improved 4.9%, 7.9%, and 10.3% in accuracy and recall respectively. And 2.6%, 4.2%, 6.6%, can recommend for users more interested in the relationship.


Social network User similarity Dynamic influence User recommendation 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xiaoquan Xiong
    • 1
    • 2
  • Mingxin Zhang
    • 2
    Email author
  • Jinlon Zheng
    • 2
  • Yongjun Liu
    • 2
  1. 1.College of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Department of Computer Science and EngineeringChangshu Institute of TechnologyChangshuChina

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