Abstract
In this paper a recommender system of personalized TV contents, named AVATAR, is presented. We propose a modular multi-agent architecture for the system, whose main novelty is the semantic reasoning about user preferences and historical logs, to improve the traditional syntactic content search. Our approach uses Semantic Web technologies – more specifically an OWL ontology – and the TV-Anytime standard to describe the TV contents. To reason about the ontology, we have defined a query language, named LIKO, for inferring knowledge from properties contained in it. In addition, we show an example of a semantic recommendation by means of some LIKO operators.
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© 2004 Springer-Verlag Berlin Heidelberg
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Blanco-Fernández, Y. et al. (2004). AVATAR: An Advanced Multi-agent Recommender System of Personalized TV Contents by Semantic Reasoning. In: Zhou, X., Su, S., Papazoglou, M.P., Orlowska, M.E., Jeffery, K. (eds) Web Information Systems – WISE 2004. WISE 2004. Lecture Notes in Computer Science, vol 3306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30480-7_43
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DOI: https://doi.org/10.1007/978-3-540-30480-7_43
Publisher Name: Springer, Berlin, Heidelberg
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