Personalized Location Selection in Large-scale Geo-social Networks

Research output: ThesisDoctoral Thesis

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In this thesis, we study the significance of location information in geo-social networks and develop efficient algorithms to accommodate location-enriched real-life applications. We also incorporate latent relationships between users and their locations to personalize user preferences to improve performances of the applications. The critical problems identified and solved in
this thesis are: (1) modelling social connections and user mobility, (2) real-time location prediction, and (3) user preference discovery and co-engaged location group search in large-scale check-in data. This research mainly concentrates on overcoming the challenges from the efficiency and generalization capabilities of geo-social analytics while dealing with large-scale social networks.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • The University of Western Australia
  • Reynolds, Mark, Supervisor
  • Mian, Ajmal, Supervisor
  • Li, Jianxin, Supervisor
  • Sellis, Timos, Supervisor, External person
Award date6 Apr 2021
Publication statusUnpublished - 2021


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