Abstract
User profiling, defined as the inference of user interests, intentions, characteristics, behaviors and preferences, is nowadays one of the most important keys in personalized services on Internet, such as segmented target advertisements. In this paper, we propose a scalable and automated technique for user ontology profiling in social networks by extracting URL content shared by users in tweets. The new approach models a user profile as a semantic ontology where user interests and intentions are represented. OpenDNS and DBpedia collective knowledge databases are utilized in order to find the interests and intentions categories of the user profile ontology, enhancing the performance of our method and taking the collective categorization of the websites. User profile ontology evolves constantly and is populated with assertions of individuals and relationships of interest and intention from these collective knowledge repositories. Experimental results indicate strongly that the proposed method automatically generates, correctly, the interests and intentions of a user profile.
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Peña, P., Del Hoyo, R., Vea-Murguía, J., González, C., Mayo, S. (2013). Automatic Ontology User Profiling for Social Networks from URLs Shared. In: Bielza, C., et al. Advances in Artificial Intelligence. CAEPIA 2013. Lecture Notes in Computer Science(), vol 8109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40643-0_18
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DOI: https://doi.org/10.1007/978-3-642-40643-0_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40642-3
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