Name
Abstract | ||
|---|---|---|
Understanding users' social relationships plays an important role in many disciplines including marketing, management science, etc., and is the fundamental context information required in many context-aware applications. However, despite significant research progress in social learning, sensing and capturing users' daily social relationships in an accurate and non-obtrusive way is still a challenging open problem. In this paper, we propose LiPS, a social learning system that exploits wireless probes emitted by the smartphones carried by users to learn their social relationships. A novel probe filtering and Skipgram-based learning algorithm is adopted to automatically construct the social graph of users in an unobtrusive way. The evaluation results show that the LiPS system is able to accurately reflect the social relationships among smartphone users. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-77380-3_84 | ADVANCES IN MULTIMEDIA INFORMATION PROCESSING - PCM 2017, PT I |
Keywords | Field | DocType |
Social learning,WiFi probe,Skipgram | Computer vision,Social relationship,Open problem,Wireless,Social graph,Computer science,Filter (signal processing),Exploit,Human–computer interaction,Social learning,Artificial intelligence | Conference |
Volume | ISSN | Citations |
10735 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 9 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chaoxi Li | 1 | 0 | 0.34 |
| Chengwen Luo | 2 | 193 | 21.49 |
| Junliang Chen | 3 | 1051 | 124.15 |
| Hande Hong | 4 | 45 | 5.73 |
| Jian-qiang Li | 5 | 433 | 48.60 |
| Long Cheng | 6 | 14 | 3.16 |