Context semantic filtering for mobile advertisement

Abstract

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. © 2010 Springer-Verlag Berlin Heidelberg.