Name
Abstract | ||
|---|---|---|
Inferring social relationship based on co-occurrence has become a focal point in the last decade. Many studies indicate that more frequently two users co-occur at non-public locations, the higher probability they are acquaintances. We find that in some spatiotemporal datasets collected by Internet of Things (IOT) devices in public locations, it's hard to distinguish co-occurrences between acquaintances and strangers. In this paper, we propose a mobility intention-based relationship inference model (MIRI) to address above challenge. We utilize mobility intention to characterize co-occurrences and propose a classification model for social relationship inference. The experimental results on real-world dataset demonstrate not only the superiority of our model, but also improve the effectiveness. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-60033-8_75 | WIRELESS ALGORITHMS, SYSTEMS, AND APPLICATIONS, WASA 2017 |
Keywords | DocType | Volume |
Social relationship, Spatiotemporal data, Mobility intention | Conference | 10251 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |