Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs
AbstractDue to the recent explosive growth of location-aware services based on mobile devices, predicting the next places of a user is of increasing importance to enable proactive information services. In this paper, we introduce a data-driven framework that aims to predict the user’s next places using his/her past visiting patterns analyzed from mobile device logs. Specifically, the notion of the spatiotemporal-periodic (STP) pattern is proposed to capture the visits with spatiotemporal periodicity by focusing on a detail level of location for each individual. Subsequently, we present algorithms that extract the STP patterns from a user’s past visiting behaviors and predict the next places based on the patterns. The experiment results obtained by using a real-world dataset show that the proposed methods are more effective in predicting the user’s next places than the previous approaches considered in most cases. View Full-Text
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Lee, S.; Lim, J.; Park, J.; Kim, K. Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs. Sensors 2016, 16, 145.
Lee S, Lim J, Park J, Kim K. Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs. Sensors. 2016; 16(2):145.Chicago/Turabian Style
Lee, Sungjun; Lim, Junseok; Park, Jonghun; Kim, Kwanho. 2016. "Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs." Sensors 16, no. 2: 145.
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