Abstract
The optimal decision about the best set of recommendations to the user is a relevant problem in different application contexts. Here we focus on a methodology for dynamically and efficiently retrieval and delivery of multimedia contents, like documents, images, video etc., which have been annotated with semantic information. We introduce the definition and the computation of the similarity between sets of concepts belonging to a common ontology and discrete optimization technique for choosing the ones to be recommended.We enable personal agents reasoning about the best set of recommendations, which are relevant to the user’s profile, so optimizing his/her satisfaction. We present its implementation in a framework that supports experts in the domain of the Cultural Heritage to augment the archaeological site with a set of multimedia contents.
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Amato, A., Di Martino, B., Scialdone, M., Venticinque, S. (2014). Personalized Recommendation of Semantically Annotated Media Contents. In: Zavoral, F., Jung, J., Badica, C. (eds) Intelligent Distributed Computing VII. Studies in Computational Intelligence, vol 511. Springer, Cham. https://doi.org/10.1007/978-3-319-01571-2_31
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DOI: https://doi.org/10.1007/978-3-319-01571-2_31
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01570-5
Online ISBN: 978-3-319-01571-2
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