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.
|Titel||Knowledge Engineering and Management by the Masses (17th International Conference, EKAW 2010, Lisbon, Portugal, October 11-15, 2010. Proceedings)|
|Redacteuren||P. Cimiano, H.S. Pinto|
|Plaats van productie||Berlin|
|ISBN van geprinte versie||978-3-642-16437-8|
|Status||Gepubliceerd - 2010|
|Naam||Lecture Notes in Computer Science|
|ISSN van geprinte versie||0302-9743|