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- W2895881388 abstract "Predicting users’ mobility trajectories is significant for service providers, such as recommendation systems for tourist routing, emergency warning, etc. However, the former researchers predict the next location merely by observing the past individual trajectories, which usually performs poor in the accuracy of trace prediction. In this paper, POIs (Points of Interest) information is used to adjust the weight parameters of the predicted results, and the rationality and precision would be improved. The cellular towers are firstly classified into seven types of functional area through POIs. Then the target user’s next possible functional area could be speculated, which acts as a supervision of the ultimate prediction outcome. We use the DP (Dirichlet Process) mixture model to identify similarity between different users and predict users’ locations by leveraging these similar users. As is shown in the results, the methods proposed above are highly adaptive and precise when being utilized to predict users’ mobility trajectories." @default.
- W2895881388 created "2018-10-26" @default.
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- W2895881388 date "2018-01-01" @default.
- W2895881388 modified "2023-10-06" @default.
- W2895881388 title "Mobility Prediction Based on POI-Clustered Data" @default.
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- W2895881388 doi "https://doi.org/10.1007/978-3-030-00557-3_7" @default.
- W2895881388 hasPublicationYear "2018" @default.
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