Publication
Title
Predicting visitors using location-based social networks
Author
Abstract
Location-based social networks (LBSN) are social networks complemented with users' location data, such as geo-tagged activity data. Predicting such activities finds application in marketing, recommendation systems, and logistics management. In this paper, we exploit LBSN data to predict future visitors at given locations. We fetch the travel history of visitors by their check-ins in LBSNs and identify five features that significantly drive the mobility of a visitor towards a location: (i) historic visits, (ii) location category, (iii) time, (iv) distance, and (v) friends' activities. We provide a visitor prediction model, CMViP, based on collective matrix factorization and influence propagation. CMViP first utilizes collective matrix factorization to map the first four features to a common latent space to find visitors having a significant potential to visit a given location. Then, it utilizes an influence-mining approach to further incorporate friends of those visitors, who are influenced by the visitors' activities and likely to follow them. Our experiments on two real-world data-sets show that our methods outperform the state of art in terms of precision and accuracy.
Language
English
Source (journal)
19th IEEE International Conference on Mobile Data Management (MDM),2018
Source (book)
19th IEEE International Conference on Mobile Data Management (MDM), 25-28 June 2018, Aalborg, Denmark
Publication
2018
Volume/pages
(2018) , p. 245-250
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identification
Creation 01.08.2018
Last edited 15.07.2021
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