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

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


Shuai Xu

Jiuxin Cao

Bo Liu

Shancang Li


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. A 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 paper, 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.


Xu, S., Cao, J., Legg, P., Liu, B., & Li, S. (2020). Venue2Vec: An efficient embedding model for fine-grained user location prediction in geo-social networks. IEEE Systems Journal, 14(2), 1740-1751.

Journal Article Type Article
Acceptance Date Apr 10, 2019
Online Publication Date May 14, 2019
Publication Date Jun 1, 2020
Deposit Date Apr 15, 2019
Publicly Available Date Jul 15, 2019
Journal IEEE Systems Journal
Print ISSN 1932-8184
Electronic ISSN 1937-9234
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 14
Issue 2
Pages 1740-1751
Keywords predictive models; semantics; context modeling; task analysis; data models; computer networks; training; geo-social networks; language model; location embedding; location prediction; random walk
Public URL
Publisher URL
Additional Information Additional Information : (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.


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