A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media-Reference-Cited by-同舟云学术

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A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media

Published:2020-08-26 Issue:4 Volume:35 Page:639-660
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

Bao Yi¹²,Huang Zhou¹²^ORCID,Li Linna³,Wang Yaoli¹²,Liu Yu¹²^ORCID

Affiliation:

1. Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China

2. Beijing Key Lab of Spatial Information Integration & Its Applications, Peking University, Beijing, China

3. Department of Geography, California State University, California, USA

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Library and Information Sciences,Geography, Planning and Development,Information Systems

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2020.1808896

Reference36 articles.

1. Alayba, A.M. et al. 2018. A combined CNN and LSTM model for arabic sentiment analysis. In: International cross-domain conference for machine learning and knowledge extraction. Hamburg, Germany: Springer, 179–191.

2. Learning significant locations and predicting user movement with GPS

3. Content-Aware Hierarchical Point-of-Interest Embedding Model for Successive POI Recommendation

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