Abstract
In this chapter, we present a recommending system that has been developed for filtering TV content provided to users on their mobile devices. This recommender is fully based on ontologies which are used to formalize both the user and her/his interests, and the audiovisual content. The developed ontologies allow matchmaking between user and content at different levels, based on three means to define user interests: categories, content description, or any combination of concepts defined in an ontology. The computation of user profiles relies on both explicit and implicit profiling, based on incremental learning of interest degrees from content usage.
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Naudet, Y., Aghasaryanb, A., Mignon, S., Toms, Y., Senot, C. (2010). Ontology-Based Profiling and Recommendations for Mobile TV. In: Wallace, M., Anagnostopoulos, I.E., Mylonas, P., Bielikova, M. (eds) Semantics in Adaptive and Personalized Services. Studies in Computational Intelligence, vol 279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11684-1_3
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DOI: https://doi.org/10.1007/978-3-642-11684-1_3
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