A kNN Based Position Prediction Method for SNS Places

  • Jong-Shin Chen
  • Huai-Yi Huang
  • Chi-Yueh HsuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12034)


With the growing popularity of Social Network Services (SNS), many researchers put effort into achieving some enhancements for these service. Systems like Facebook (FB), Google Maps, Twitter, Instagram, Foursquare, LinkedIn and so forth are the most acclaimed ones. These services generally contain a large number of geographical places, such as FB check-in places, Google Maps places, Foursquare check-in places. However, it is a very difficult to fast to do place positioning. Notably, place positioning indicates to find the specific geographical area where places are inside to this area. Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. With ML, the k-nearest neighbors (kNN) algorithm is a non-parametric method used for classification. Accordingly, in this study, we propose a kNN Based Position Prediction Method for SNS Places.


Social Network Services k nearest neighbors Position Prediction 



This research was partially supported by the Ministry Of Science and Technology, Taiwan (ROC), under contract no.: MOST 108-2410-H-324-007.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Information and Communication EngineeringChaoyang University of TechnologyTaichungTaiwan, R.O.C.
  2. 2.Department of Leisure Services ManagementChaoyang University of TechnologyTaichungTaiwan, R.O.C.

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