Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3390/s16020145 | SENSORS |
Keywords | Field | DocType |
spatiotemporal patterns,Markov chain,gapped sequence mining,movement patterns,next place prediction | Information system,Data mining,Computer science,Markov chain,Mobile device,Spatiotemporal pattern | Journal |
Volume | Issue | ISSN |
16 | 2.0 | 1424-8220 |
Citations | PageRank | References |
10 | 0.55 | 23 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sungjun Lee | 1 | 10 | 0.89 |
| junseok | 2 | 87 | 15.57 |
| Jonghun Park | 3 | 491 | 37.86 |
| Kwanho Kim | 4 | 361 | 37.49 |