Measuring User Similarity with Trajectory Patterns: Principles and New Metrics

  • Xihui Chen
  • Ruipeng Lu
  • Xiaoxing Ma
  • Jun Pang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8709)


The accumulation of users’ whereabouts in location-based applications has made it possible to construct user mobility profiles. Trajectory patterns, i.e., traces of places of interest that a user frequently visits, are among the most popular models of mobility profiles. In this paper, we revisit measuring user similarity using trajectory patterns, which is an important supplement for friend recommendation in on-line social networks. Specifically, we identify and formalise a number of basic principles that should hold when quantifying user similarity with trajectory patterns. These principles allow us to evaluate existing metrics in the literature and demonstrate their insufficiencies. Then we propose for the first time a new metric that respects all the identified principles. The metric is extended to deal with location semantics. Through experiments on a real-life trajectory dataset, we show the effectiveness of our new metrics.


User Mobility User Similarity Trajectory Pattern Maximal Pattern Semantic Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xihui Chen
    • 1
  • Ruipeng Lu
    • 2
  • Xiaoxing Ma
    • 3
  • Jun Pang
    • 1
    • 2
  1. 1.Interdisciplinary Centre for Security, Reliability and TrustUniversity of LuxembourgLuxembourg
  2. 2.Faculty of Science, Technology and CommunicationUniversity of LuxembourgLuxembourg
  3. 3.State Key Laboratory for Novel Software Technology, Department of Computer Science and TechnologyNanjing UniversityChina

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