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. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Codina, V., & Ceccaroni, L. (2010). A recommendation system for the semantic web. In Advances in Intelligent and Soft Computing (Vol. 79, pp. 45–52). https://doi.org/10.1007/978-3-642-14883-5_6
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