Description

Location-based social networks (LBSNs) have attracted an increasing number of users in recent years, resulting in large amounts of geographical and social data. Such LBSN data provide an unprecedented opportunity

Location-based social networks (LBSNs) have attracted an increasing number of users in recent years, resulting in large amounts of geographical and social data. Such LBSN data provide an unprecedented opportunity to study the human movement from their socio-spatial behavior, in order to improve location-based applications like location recommendation. As users can check-in at new places, traditional work on location prediction that relies on mining a user’s historical moving trajectories fails as it is not designed for the cold-start problem of recommending new check-ins.

application/pdf

Download count: 0

Details

Contributors
Date Created
  • 2015-03-01
Resource Type
  • Text
  • Collections this item is in
    Identifier
    • Digital object identifier: 10.1007/s10618-014-0343-4
    • Identifier Type
      International standard serial number
      Identifier Value
      1384-5810
    • Identifier Type
      International standard serial number
      Identifier Value
      1573-756X
    Note

    Citation and reuse

    Cite this item

    This is a suggested citation. Consult the appropriate style guide for specific citation guidelines.

    Gao, Huiji, Tang, Jiliang, & Liu, Huan (2015). Addressing the cold-start problem in location recommendation using geo-social correlations. DATA MINING AND KNOWLEDGE DISCOVERY, 29(2), 299-323. http://dx.doi.org/10.1007/s10618-014-0343-4

    Machine-readable links