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Published in: World Wide Web 3/2020

31-01-2020

Survey on user location prediction based on geo-social networking data

Authors: Shuai Xu, Xiaoming Fu, Jiuxin Cao, Bo Liu, Zhixiao Wang

Published in: World Wide Web | Issue 3/2020

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Abstract

With the popularity of smart mobile terminals and advances in wireless communication and positioning technologies, Geo-Social Networks (GSNs), which combine location awareness and social service functions, have become increasingly prevalent. The increasing amount of user and location information in GSNs makes the information overload phenomenon more and more serious. Although massive user-generated data brings convenience to users’ social and travel activities, it also causes certain trouble for their daily life. In this context, users are expecting smarter mobile applications, so that the location information can be employed to perceive their surrounding environment intelligently and further mine their behavior patterns in GSNs, which ultimately provides personalized location-based services for users. Therefore, research on user location prediction comes into existence and has received extensive and in-depth attention from researchers. Through systematically analyzing the location data carried by user check-ins and comments, user location prediction can mine various user behavior patterns and personal preferences, thus determining the visiting location of users in the future. Research on user location prediction is still in the ascendant and it has become an important topic of common concern in both academia and industry. This survey takes Geo-social networking data as the focal point to elaborate the recent progress in user location prediction from multiple aspects such as problem categories, data sources, feature extraction, mathematical models and evaluation metrics. Besides, the difficulties to be studied and the future developmental trends of user location prediction are discussed.
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Footnotes
1
In this article, the three terms ‘location’, ‘POI’ and ‘venue’ can be used interchangeably unless otherwise stated.
 
8
Tabelog is a restaurant information website for those who want to choose the right restaurant for their needs.
 
9
A famous travelogue website offering rich descriptions about landmarks and traveling experience written by users.
 
10
Code of SAE-NAD is available at https://​github.​com/​allenjack/​SAE-NAD; Code of CARA is available at https://​github.​com/​feay1234/​CARA; Code of LBSN2Vec is available at https://​github.​com/​eXascaleInfolab/​LBSN2Vec
 
11
Yelp dataset challenge round 12, https://​www.​yelp.​com/​dataset/​challenge, access date: January 2019.
 
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Metadata
Title
Survey on user location prediction based on geo-social networking data
Authors
Shuai Xu
Xiaoming Fu
Jiuxin Cao
Bo Liu
Zhixiao Wang
Publication date
31-01-2020
Publisher
Springer US
Published in
World Wide Web / Issue 3/2020
Print ISSN: 1386-145X
Electronic ISSN: 1573-1413
DOI
https://doi.org/10.1007/s11280-019-00777-8

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