Can your friends predict where you will be?

Lei Cao, James She

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

With the development of mobile device and wireless networks, user location becomes increasingly valuable in enhancing user experience, system performance and resource allocation. Location-based services have been not only an important perspective of social media, but also a significant contributor to big data analysis. Location prediction, as an interesting topic, can help improve system performance and user experience in location-based services. Existing algorithms on such prediction focus mostly on exploring regularity in users' movement history without taking advantage of the research on social networks, which can provide information on other factors such as peer influence in human mobility. In this work, the aim is to propose an enhanced location prediction model based on both users' mobility patterns and social network information and the proposed algorithm shows a significant improvement over existing ones.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Conference on Internet of Things, iThings 2014, 2014 IEEE International Conference on Green Computing and Communications, GreenCom 2014 and 2014 IEEE International Conference on Cyber-Physical-Social Computing, CPS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages450-455
Number of pages6
ISBN (Electronic)9781479959679
DOIs
Publication statusPublished - 12 Mar 2014
Externally publishedYes
Event2014 IEEE International Conference on Internet of Things, iThings 2014, Collocated with 2014 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2014 and 2014 IEEE International Conference on Green Computing and Communications, GreenCom 2014 - Taipei, Taiwan, Province of China
Duration: 1 Sept 20143 Sept 2014

Publication series

NameProceedings - 2014 IEEE International Conference on Internet of Things, iThings 2014, 2014 IEEE International Conference on Green Computing and Communications, GreenCom 2014 and 2014 IEEE International Conference on Cyber-Physical-Social Computing, CPS 2014

Conference

Conference2014 IEEE International Conference on Internet of Things, iThings 2014, Collocated with 2014 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2014 and 2014 IEEE International Conference on Green Computing and Communications, GreenCom 2014
Country/TerritoryTaiwan, Province of China
CityTaipei
Period1/09/143/09/14

Keywords

  • Big data
  • Location prediction
  • Social network analysis

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