Abstract
Recommender systems face up to current information overload by selecting automatically items that match the personal preferences of each user. The so-called content-based recommenders suggest items similar to those the user liked in the past, by resorting to syntactic matching mechanisms. The rigid nature of such mechanisms leads to recommend only items that bear a strong resemblance to those the user already knows. In this paper, we propose a novel content-based strategy that diversifies the offered recommendations by employing reasoning mechanisms borrowed from the Semantic Web. These mechanisms discover extra knowledge about the user’s preferences, thus favoring more accurate and flexible personalization processes. Our approach is generic enough to be used in a wide variety of personalization applications and services, in diverse domains and recommender systems. The proposed reasoning-based strategy has been empirically evaluated with a set of real users. The obtained results evidence computational feasibility and significant increases in recommendation accuracy w.r.t. existing approaches where our reasoning capabilities are disregarded.
Work funded by the Ministerio de Educación y Ciencia (Gobierno de España) research project TSI2007-61599, by the Consellería de Educación e Ordenación Universitaria (Xunta de Galicia) incentives file 2007/000016-0, and by the Programa de Promoción Xeral da Investigación de la Consellería de Innovación, Industria e Comercio (Xunta de Galicia) PGIDIT05PXIC32204PN.
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Blanco-Fernández, Y., Pazos-Arias, J.J., Gil-Solla, A., Ramos-Cabrer, M., López-Nores, M. (2008). Semantic Reasoning: A Path to New Possibilities of Personalization. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds) The Semantic Web: Research and Applications. ESWC 2008. Lecture Notes in Computer Science, vol 5021. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68234-9_52
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DOI: https://doi.org/10.1007/978-3-540-68234-9_52
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