HisRect: Features from Historical Visits and Recent Tweet for Co-Location Judgement

Pengfei Li, Hua Lu, Qian Zheng, Shijian Li, Gang Pan

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.
Original languageEnglish
Title of host publicationProceedings 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020, Dallas, TX, USA, April 20-24, 2020
Number of pages2
PublisherIEEE
Publication date2020
Pages2034-2035
ISBN (Electronic)978-1-7281-2903-7, 978-1-7281-2904-4
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event36th IEEE International Conference on Data Engineering - Online, Dallas, United States
Duration: 20 Apr 202024 Apr 2020
Conference number: 36
https://www.utdallas.edu/icde/

Conference

Conference36th IEEE International Conference on Data Engineering
Number36
LocationOnline
CountryUnited States
CityDallas
Period20/04/202024/04/2020
OtherOnline conference
Internet address
SeriesProceedings of the International Conference on Data Engineering
ISSN1063-6382

Bibliographical note

This is a poster version of a full IEEE TKDE paper.

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