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Auflistung nach Autor:in "Houben, Geert-Jan"

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  • Konferenzbeitrag
    Mashing up user data in the Grapple User Modeling Framework
    (17th Workshop on Adaptivity and User Modeling in Interactive Systems, 2009) Abel, Fabian; Heckmann, Dominik; Herder, Eelco; Hidders, Jan; Houben, Geert-Jan; Krause, Daniel; Leonardi, Erwin; van der Sluijs, Kees
    In this paper we demonstrate the Grapple User Modeling Framework (GUMF), which exploits Semantic Web technologies and Web 2.0 paradigms to model users across different applications and domains. It introduces novel features such as dataspaces, which logically bundle user data, and user pipes, which allow to mash up user data from different sources.
  • Konferenzbeitrag
    Mining Twitter for Cultural Patterns
    (Mensch & Computer 2012 – Workshopband: interaktiv informiert – allgegenwärtig und allumfassend!?, 2012) Ilina, Elena; Abel, Fabian; Houben, Geert-Jan
    Adaptive applications rely on the knowledge of their users, their needs and differences. For instance, in the scope of the ImReal project, a training process is adapted to users’ origins using information on user cultural backgrounds. For inferring culture-specific information from available microblogging content, we monitor the usage of Twitter elements such as hashtags, web links and user mentions. We analyze how users from different cultural groups employ these elements when they tweet. This allows us to get insights on microblogging patterns for different cultural groups of Twitter users and an outlook into user preferences and traits towards sharing content with others, time preferences, and social networking attitudes. Potentially, such information can be used for adapting software applications in accord with user culture-specific behavioral traits.
  • Konferenzbeitrag
    Mining Twitter for Cultural Patterns
    (ABIS 2012, 2012) Ilina, Elena; Abel, Fabian; Houben, Geert-Jan
    Adaptive applications rely on the knowledge of their users, their needs and differences. For instance, in the scope of the ImReal 1 project, a training process is adapted to users’ origins using information on user cultural backgrounds. For inferring culture-specific information from available microblogging content, we monitor the usage of Twitter elements such as hashtags, web links and user mentions. We analyze how users from different cultural groups employ these elements when they tweet. This allows us to get insights on microblogging patterns for different cultural groups of Twitter users and an outlook into user preferences and traits towards sharing content with others, time preferences, and social networking attitudes. Potentially, such information can be used for adapting software applications in accord with user culture-specific behavioral traits.