Context Semantic Filtering for Mobile Advertisement

  • Andrés Moreno
  • Harold Castro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6428)


Mobile advertisement causes an information overload problem that is addressed by information filtering systems. Semantical filtering systems stand out in comparison to traditional approaches thanks to their use of ontologies as knowledge model improving automatic user profiling and content matching processes in filtering. This position paper identifies some enhancement opportunities related to these two processes, manifold: The formulation of a semantic similarity metric that points out the importance of the relations and properties present in the knowledge domain and a extension in the contextual information included so far in filtering systems. The expected result of the work is to improve the overall effectiveness of semantic information filtering systems, tested in the mobile advertisement scenario.


Recommender System Data Item User Satisfaction Mobile Environment Knowledge Model 
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-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Andrés Moreno
    • 1
    • 2
  • Harold Castro
    • 1
  1. 1.Universidad de los AndesBogotáColombia
  2. 2.I3S, Université de Nice Sophia AntipolisFrance

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