Abstract
Recommendation systems can take advantage of semantic reasoning-capabilities to overcome common limitations of current systems and improve the recommendations’ quality. In this paper, we present a personalized-recommendation system, a system that makes use of representations of items and user-profiles based on ontologies in order to provide semantic applications with personalized services. The recommender uses domain ontologies to enhance the personalization: on the one hand, user’s interests are modeled in a more effective and accurate way by applying a domain-based inference method; on the other hand, the matching algorithm used by our content-based filtering approach, which provides a measure of the affinity between an item and a user, is enhanced by applying a semantic similarity method. The experimental evaluation on the Netflix movie-dataset demonstrates that the additional knowledge obtained by the semantics-based methods of the recommender contributes to the improvement of recommendation’s quality in terms of accuracy.
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Codina, V., Ceccaroni, L. (2010). A Recommendation System for the Semantic Web. In: de Leon F. de Carvalho, A.P., Rodríguez-González, S., De Paz Santana, J.F., Rodríguez, J.M.C. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 79. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14883-5_6
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DOI: https://doi.org/10.1007/978-3-642-14883-5_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14882-8
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