Abstract
Analyzing large volume of trajectory data plays an important role in understanding user behaviors and providing personalized recommendations. However, existing work faces many challenges in important location discovery processing speed and accuracy. This paper proposes a general computing framework to improve the accuracy of occupational and residential location detection in cellular network. An important location discovery module and an index structure is included, which improves the efficiency and accuracy. A mining algorithm MMA (Matrix base Mining Algorithm) is proposed, which improves the accuracy of user important location. Experimental evaluation shows that the proposed algorithm has higher accuracy and efficiency in real environment.
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The work is supported by the National Nature Science Foundation of China (41571401).
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Zhang, X., Hu, Y. (2019). Discovery of Important Location from Massive Trajectory Data Based on Mediation Matrix. In: Silhavy, R. (eds) Software Engineering Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol 984. Springer, Cham. https://doi.org/10.1007/978-3-030-19807-7_35
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DOI: https://doi.org/10.1007/978-3-030-19807-7_35
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