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' His-Rect features. Extensive experiments on real Twitter data suggest that our HisRect features and SSL framework are highly effective at deciding co-locations.
Originalsprog | Engelsk |
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Titel | Proceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020 |
Antal sider | 2 |
Forlag | IEEE |
Publikationsdato | apr. 2020 |
Sider | 2034-2035 |
Artikelnummer | 9101767 |
ISBN (Elektronisk) | 978-1-7281-2903-7, 978-1-7281-2904-4 |
DOI | |
Status | Udgivet - apr. 2020 |
Udgivet eksternt | Ja |
Begivenhed | 36th IEEE International Conference on Data Engineering - Online, Dallas, USA Varighed: 20 apr. 2020 → 24 apr. 2020 Konferencens nummer: 36 https://www.utdallas.edu/icde/ |
Konference
Konference | 36th IEEE International Conference on Data Engineering |
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Nummer | 36 |
Lokation | Online |
Land/Område | USA |
By | Dallas |
Periode | 20/04/2020 → 24/04/2020 |
Andet | Online konference |
Internetadresse |
Navn | Proceedings of the International Conference on Data Engineering |
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ISSN | 1063-6382 |