Personalized Location Selection in Large-scale Geo-social Networks

Nur Al Hasan Haldar

doi:10.26182/v0km-t497

Personalized Location Selection in Large-scale Geo-social Networks

Nur Al Hasan Haldar

School of Physics, Mathematics and Computing

Research output: Thesis › Doctoral Thesis

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Abstract

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 language	English
Qualification	Doctor of Philosophy
Awarding Institution	The University of Western Australia
Supervisors/Advisors	Reynolds, Mark, Supervisor Mian, Ajmal, Supervisor Li, Jianxin, Supervisor Sellis, Timos, Supervisor, External person
Award date	6 Apr 2021
DOIs	https://doi.org/10.26182/v0km-t497
Publication status	Unpublished - 2021

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10.26182/v0km-t497

THESIS - DOCTOR OF PHILOSOPHY - HALDAR, Nur Al Hasan - 2021
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title = "Personalized Location Selection in Large-scale Geo-social Networks",

abstract = "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 inthis 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.",

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AB - 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 inthis 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.

KW - location selection

KW - geo-social network

KW - Location prediction

U2 - 10.26182/v0km-t497

DO - 10.26182/v0km-t497

M3 - Doctoral Thesis

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Personalized Location Selection in Large-scale Geo-social Networks

Abstract

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