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Location Tracking Prediction of Network Users Based on Online Learning Method With Python

Location Tracking Prediction of Network Users Based on Online Learning Method With Python

Xin Xu, Hui Lu
Copyright: © 2021 |Pages: 17
ISBN13: 9781799880479|ISBN10: 1799880478|EISBN13: 9781799880974
DOI: 10.4018/978-1-7998-8047-9.ch038
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MLA

Xu, Xin, and Hui Lu. "Location Tracking Prediction of Network Users Based on Online Learning Method With Python." Research Anthology on Developing Effective Online Learning Courses, edited by Information Resources Management Association, IGI Global, 2021, pp. 790-806. https://doi.org/10.4018/978-1-7998-8047-9.ch038

APA

Xu, X. & Lu, H. (2021). Location Tracking Prediction of Network Users Based on Online Learning Method With Python. In I. Management Association (Ed.), Research Anthology on Developing Effective Online Learning Courses (pp. 790-806). IGI Global. https://doi.org/10.4018/978-1-7998-8047-9.ch038

Chicago

Xu, Xin, and Hui Lu. "Location Tracking Prediction of Network Users Based on Online Learning Method With Python." In Research Anthology on Developing Effective Online Learning Courses, edited by Information Resources Management Association, 790-806. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-8047-9.ch038

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Abstract

Aiming at the problem that the precision and recall rate of traditional prediction methods are low and its low prediction efficiency, a Python-based trajectory tracking prediction method of online learning network user location is proposed. First, troubleshooting terminal programs of online learning network user by programming in Python (computer programming language) structure, the location trajectory data of online learning network user is spatially processed. In this way, features of time-related, spatial correlation, social relationship correlation, and user preference characteristics are extracted respectively to realize feature normalization processing. Second, on this basis, the cosine similarity is used to calculate the similarity of user behavior trajectory. According to K-MEANS (hard clustering algorithm), the time dimension is considered. Finally, the clustering result of users' behavior trajectory based on the sign-in data is compared with a preset threshold to predict the online user location trajectory. The experimental results show that the proposed method normalizes the user's trajectory, combines the time segment, and compares it with the preset threshold, which does not only improve the prediction efficiency but also obtains higher and more feasible precision and recall rate.

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