A Thematic Approach to User Similarity Built on Geosocial Check-ins

  • Grant McKenzieEmail author
  • Benjamin Adams
  • Krzysztof Janowicz
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Computing user similarity is key for personalized location-based recommender systems and geographic information retrieval. So far, most existing work has focused on structured or semi-structured data to establish such measures. In this work, we propose topic modeling to exploit sparse, unstructured data, e.g., tips and reviews, as an additional feature to compute user similarity. Our model employs diagnosticity weighting based on the entropy of topics in order to assess the role of commonalities and variabilities between similar users. Finally, we offer a validation technique and results using data from the location-based social network Foursquare.


Latent Dirichlet Allocation Online Social Networking User Similarity Topic Distribution Topic Signature 
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 2013

Authors and Affiliations

  • Grant McKenzie
    • 1
    Email author
  • Benjamin Adams
    • 2
  • Krzysztof Janowicz
    • 1
  1. 1.Department of GeographyUniversity of CaliforniaSanta BarbaraUSA
  2. 2.National Center for Ecological Analysis and Synthesis (NCEAS)University of CaliforniaSanta BarbaraUSA

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