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Predicting Visitors Using Location-Based Social Networks

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review


  • Muhammad Aamir Saleem, Aalborg University
  • ,
  • Felipe Soares da Costa, Aalborg University
  • ,
  • Peter Dolog, Aalborg University
  • ,
  • Panagiotis Karras
  • Torben Bach Pedersen, Aalborg University, Denmark
  • 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.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 19th International Conference on Mobile Data Management, MDM 2018
Number of pages6
Publication year13 Jul 2018
ISBN (print)978-1-5386-4134-7
ISBN (Electronic)978-1-5386-4133-0
Publication statusPublished - 13 Jul 2018
Event19th IEEE International Conference on Mobile Data Management - Aalborg, Denmark
Duration: 26 Jun 201828 Jun 2018
Conference number: 19


Conference19th IEEE International Conference on Mobile Data Management

    Research areas

  • Collective matrix factorization, Influence propagation, Location based Social Networks, Visitor prediction

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