@inproceedings{f2abd6a4142d4cd48a217f231bd99c41,
title = "Enhancing content-based recommendation with the task model of classification",
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.",
author = "Y. Wang and Shenghui Wang and N. Stash and L.M. Aroyo and A.Th. Schreiber",
year = "2010",
doi = "10.1007/978-3-642-16438-5_33",
language = "English",
isbn = "978-3-642-16437-8",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "431--440",
editor = "P. Cimiano and H.S. Pinto",
booktitle = "Knowledge Engineering and Management by the Masses (17th International Conference, EKAW 2010, Lisbon, Portugal, October 11-15, 2010. Proceedings)",
address = "Germany",
}