Online Friends Recommendation Based on Geographic Trajectories and Social Relations

  • Shi Feng
  • Dajun Huang
  • Kaisong Song
  • Daling Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8346)


With the rapid development of GPS-enabled mobile devices, people like to publish online data with geographic information. The traditional online friend recommendation methods usually focus on the shared interests, topics or social network links, but neglect the more and more important geographic information. In this paper, we focus on users’ geographic trajectories that consisting of a series of positions in time order. We reduce the length of each trajectory by clustering the points and normalize every trajectory according to its positions and time in the trajectory. The similarity between trajectories is computed based on the distance of each corresponding point pair in the respective trajectory and the trajectories’ trends. The potential online friends are recommended based on the trajectory similarity and social network structures. Extensive experiment results have validated the feasibility and effectiveness of our proposed approach.


Friend Recommendation Geographic Trajectory Social Network 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shi Feng
    • 1
    • 2
  • Dajun Huang
    • 1
  • Kaisong Song
    • 1
  • Daling Wang
    • 1
    • 2
  1. 1.School of Information Science and EngineeringNortheastern UniversityChina
  2. 2.Key Laboratory of Medical Image ComputingNortheastern University, Ministry of EducationShenyangChina

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