Abstract
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.
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Acknowledgment
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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Chen, JS., Huang, HY., Hsu, CY. (2020). A kNN Based Position Prediction Method for SNS Places. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12034. Springer, Cham. https://doi.org/10.1007/978-3-030-42058-1_22
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