Enhancing content-based recommendation with the task model of classication

Y. Wang, S. Wang, N. Stash, L.M. Aroyo, A.Th. Schreiber

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Abstract

In this paper, we define reusable inference steps for content-based recommender systems based on semantically-enriched collections. We show an instantiation in the case of recommending artworks and concepts based on a museum domain ontology and a user profile consisting of rated artworks and rated concepts. The recommendation task is split into four inference steps: realization, classification by concepts, classification by instances, and retrieval. Our approach is evaluated on real user rating data. We compare the results with the standard content-based recommendation strategy in terms of accuracy and discuss the added values of providing serendipitous recommendations and supporting more complete explanations for recommended items. © 2010 Springer-Verlag.
Original languageEnglish
Title of host publicationProceedings of the 17th International Conference on Knowledge Engineering and Knowledge Management
EditorsP. Cimiano, H.S. Pinto
Place of PublicationLisbon
Pages431-440
DOIs
Publication statusPublished - 2010
Event17th International Conference on Knowledge Engineering and Knowledge Management (EKAW2010) - Lisbon
Duration: 1 Jan 20101 Jan 2010

Conference

Conference17th International Conference on Knowledge Engineering and Knowledge Management (EKAW2010)
Period1/01/101/01/10

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