Name
Abstract | ||
|---|---|---|
This study explores the problem of co-location judgement, i.e., to decide whether two Twitter users are co-located at some point-of-interest (POI). We extract novel features, named HisRect, from users' historical visits and recent tweets: The former has impact on where a user visits in general, whereas the latter gives more hints about where a user is currently. To alleviate the issue of data scarcity, a semi-supervised learning (SSL) framework is designed to extract HisRect features. Moreover, we use an embedding neural network layer to decide co-location based on the difference between two users' HisRect features. Extensive experiments on real Twitter data suggest that our HisRect features and SSL framework are highly effective at deciding co-locations. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICDE48307.2020.00236 | 2020 IEEE 36TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2020) |
DocType | ISSN | Citations |
Conference | 1084-4627 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng-Fei Li | 1 | 56 | 20.94 |
| Hua Lu | 2 | 1380 | 83.74 |
| Qian Zheng | 3 | 44 | 13.91 |
| Shijian Li | 4 | 1155 | 69.34 |
| Gang Pan | 5 | 1501 | 123.57 |