Venue2Vec: An efficient embedding model for fine-grained user location prediction in geo-social networks

Xu, Shuai.; Cao, Jiuxin.; Li, Shancang.; Legg, Phil.; Liu, Bo.

doi:10.1109/JSYST.2019.2913080

Authors

Shuai Xu

Jiuxin Cao

Shancang Li Shancang.Li@uwe.ac.uk
Senior Lecturer in Computer Forensics and Security

Phil Legg Phil.Legg@uwe.ac.uk
Associate Professor in Cyber Security

Bo Liu

Abstract

Abstract—Geo-Social networks (GSN) significantly improve
location-aware capability of services by offering geo-located content based on the huge volumes of data generated in the GSN. The problem of user location prediction based on user generated data in GSN has been extensively studied. However, existing studies are either concerning predicting users’ next check-in location or predicting their future check-in location at a given
time with coarse granularity. An unified model that can predict both scenarios with fine granularity is quite rare. Also, due to the heterogeneity of multiple factors associated with both locations and users, how to efficiently incorporate these information still
remains challenging.
Inspired by the recent success of word embedding in natural language processing, in this work, we propose a novel embedding model called Venue2Vec which automatically incorporates temporal-spatial context, semantic information, and sequential relations for fine-grained user location prediction. Locations of
the same type, and those that are geographically close or often visited successively by users will be situated closer within the embedding space. Based on our proposed Venue2Vec model, we design techniques that allow for predicting a user’s next check in location, and also their future check-in location at a given time. We conduct experiments on three real-world GSN datasets
to verify the performance of the proposed model. Experimental results on both tasks show that Venue2Vec model outperforms several state-of-the-art models on various evaluation metrics. Furthermore, we show how the Venue2Vec model can be more time efficient due to being parallelizable.

Journal Article Type	Article
Publication Date	May 14, 2019
Journal	IEEE Systems Journal
Print ISSN	1932-8184
Publisher	Institute of Electrical and Electronics Engineers
Peer Reviewed	Peer Reviewed
Institution Citation	Xu, S., Cao, J., Li, S., Legg, P., & Liu, B. (2019). Venue2Vec: An efficient embedding model for fine-grained user location prediction in geo-social networks. IEEE Systems Journal, https://doi.org/10.1109/JSYST.2019.2913080
DOI	https://doi.org/10.1109/JSYST.2019.2913080
Keywords	social network, security, IoT, GSN
Publisher URL	http://dx.doi.org/10.1109/JSYST.2019.2913080
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