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.
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Wang, Y., Wang, S., Stash, N., Aroyo, L., Schreiber, G. (2010). Enhancing Content-Based Recommendation with the Task Model of Classification. In: Cimiano, P., Pinto, H.S. (eds) Knowledge Engineering and Management by the Masses. EKAW 2010. Lecture Notes in Computer Science(), vol 6317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16438-5_33
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DOI: https://doi.org/10.1007/978-3-642-16438-5_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16437-8
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