title: Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs.
abstract: Due 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.
authors.affiliation: Department of Industrial Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea. zaregn81@snu.ac.kr.
authors.forename: Sungjun
authors.initials: S
authors.lastname: Lee
authors.affiliation: Department of Industrial Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea. kasia05@snu.ac.kr.
authors.forename: Junseok
authors.initials: J
authors.lastname: Lim
authors.affiliation: Department of Industrial Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea. jonghun@snu.ac.kr.
authors.forename: Jonghun
authors.initials: J
authors.lastname: Park
authors.affiliation: Department of Industrial &amp; Management Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Korea. khokim@inu.ac.kr.
authors.forename: Kwanho
authors.initials: K
authors.lastname: Kim
keywords: Markov chain; gapped sequence mining; movement patterns; next place prediction; spatiotemporal patterns