Enhancing content-based recommendation with the task model of classication

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

doi:10.1007/978-3-642-16438-5_33

Enhancing content-based recommendation with the task model of classication

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

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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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 language	English
Title of host publication	Proceedings of the 17th International Conference on Knowledge Engineering and Knowledge Management
Editors	P. Cimiano, H.S. Pinto
Place of Publication	Lisbon
Pages	431-440
DOIs	https://doi.org/10.1007/978-3-642-16438-5_33
Publication status	Published - 2010
Event	17th International Conference on Knowledge Engineering and Knowledge Management (EKAW2010) - Lisbon Duration: 1 Jan 2010 → 1 Jan 2010

Conference

Conference	17th International Conference on Knowledge Engineering and Knowledge Management (EKAW2010)
Period	1/01/10 → 1/01/10

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10.1007/978-3-642-16438-5_33

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Cite this

@inproceedings{8a5d5b20054e48dd8c30216f15c209a4,

title = "Enhancing content-based recommendation with the task model of classication",

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. {\textcopyright} 2010 Springer-Verlag.",

author = "Y. Wang and S. 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",

pages = "431--440",

editor = "P. Cimiano and H.S. Pinto",

booktitle = "Proceedings of the 17th International Conference on Knowledge Engineering and Knowledge Management",

note = "17th International Conference on Knowledge Engineering and Knowledge Management (EKAW2010) ; Conference date: 01-01-2010 Through 01-01-2010",

}

Wang, Y, Wang, S, Stash, N, Aroyo, LM & Schreiber, AT 2010, Enhancing content-based recommendation with the task model of classication. in P Cimiano & HS Pinto (eds), Proceedings of the 17th International Conference on Knowledge Engineering and Knowledge Management. Lisbon, pp. 431-440, 17th International Conference on Knowledge Engineering and Knowledge Management (EKAW2010), 1/01/10. https://doi.org/10.1007/978-3-642-16438-5_33

Enhancing content-based recommendation with the task model of classication. / Wang, Y.; Wang, S.; Stash, N.; Aroyo, L.M.; Schreiber, A.Th.

Proceedings of the 17th International Conference on Knowledge Engineering and Knowledge Management. ed. / P. Cimiano; H.S. Pinto. Lisbon, 2010. p. 431-440.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Enhancing content-based recommendation with the task model of classication

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AU - Schreiber, A.Th.

PY - 2010

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N2 - 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.

AB - 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.

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DO - 10.1007/978-3-642-16438-5_33

M3 - Conference contribution

SP - 431

EP - 440

BT - Proceedings of the 17th International Conference on Knowledge Engineering and Knowledge Management

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T2 - 17th International Conference on Knowledge Engineering and Knowledge Management (EKAW2010)

Y2 - 1 January 2010 through 1 January 2010

ER -

Enhancing content-based recommendation with the task model of classication

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