Publication Type
PhD Dissertation
Version
publishedVersion
Publication Date
5-2018
Abstract
This dissertation addresses the modeling of latent characteristics of locations to describe the mobility of users of location-based social networking platforms. With many users signing up location-based social networking platforms to share their daily activities, these platforms become a gold mine for researchers to study human visitation behavior and location characteristics. Modeling such visitation behavior and location characteristics can benefit many use- ful applications such as urban planning and location-aware recommender sys- tems. In this dissertation, we focus on modeling two latent characteristics of locations, namely area attraction and neighborhood competition effects using location-based social network data. Our literature survey reveals that previous researchers did not pay enough attention to area attraction and neighborhood competition effects. Area attraction refers to the ability of an area with mul- tiple venues to collectively attract check-ins from users, while neighborhood competition represents the need for a venue to compete with its neighbors in the same area for getting check-ins from users.
Keywords
Social Network, Data mining, Location-based social network, User movement, Neighbour competition, Area attraction
Degree Awarded
PhD in Information Systems
Discipline
OS and Networks | Social Media
Supervisor(s)
LIM, Ee Peng
Publisher
Singapore Management University
City or Country
Singapore
Citation
DOAN, Thanh Nam.
Learning latent characteristics of locations using location-based social networking data. (2018).
Available at: https://ink.library.smu.edu.sg/etd_coll/176
Copyright Owner and License
Author
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.