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Panagiotis Karras

Predicting Visitors Using Location-Based Social Networks

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  • Muhammad Aamir Saleem, Aalborg Universitet
  • ,
  • Felipe Soares da Costa, Aalborg Universitet
  • ,
  • Peter Dolog, Aalborg Universitet
  • ,
  • Panagiotis Karras
  • Torben Bach Pedersen, Aalborg Universitet, Danmark
  • Toon Calders, Université Libre de Bruxelles, Belgium, University of Antwerp, Antwerp

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.

TitelProceedings - 2018 IEEE 19th International Conference on Mobile Data Management, MDM 2018
Antal sider6
Udgivelsesår13 jul. 2018
ISBN (trykt)978-1-5386-4134-7
ISBN (Elektronisk)978-1-5386-4133-0
StatusUdgivet - 13 jul. 2018
Begivenhed19th IEEE International Conference on Mobile Data Management - Aalborg, Danmark
Varighed: 26 jun. 201828 jun. 2018
Konferencens nummer: 19


Konference19th IEEE International Conference on Mobile Data Management

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