Enhancing content-based recommendation with the task model of classification

Yiwen Wang; Shenghui Wang; Natalia Stash; Lora Aroyo; Guus Schreiber

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

Enhancing content-based recommendation with the task model of classification

Yiwen Wang^*, Shenghui Wang, Natalia Stash, Lora Aroyo, Guus Schreiber

^*Corresponding author for this work

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

15 Citations (Scopus)

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.

Original language	English
Title of host publication	Knowledge Engineering and Management by the Masses - 17th International Conference, EKAW 2010, Proceedings
Pages	431-440
Number of pages	10
ISBN (Electronic)	978-3-642-16438-5
DOIs	https://doi.org/10.1007/978-3-642-16438-5_33
Publication status	Published - 22 Dec 2010
Externally published	Yes
Event	17th International Conference on Knowledge Engineering and Management by the Masses, EKAW 2010 - Lisbon, Portugal Duration: 11 Oct 2010 → 15 Oct 2010 Conference number: 17

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6317 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Knowledge Engineering and Management by the Masses, EKAW 2010
Abbreviated title	EKAW 2010
Country/Territory	Portugal
City	Lisbon
Period	11/10/10 → 15/10/10

Access to Document

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

Cite this

Wang, Y., Wang, S., Stash, N., Aroyo, L., & Schreiber, G. (2010). Enhancing content-based recommendation with the task model of classification. In Knowledge Engineering and Management by the Masses - 17th International Conference, EKAW 2010, Proceedings (pp. 431-440). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6317 LNAI). https://doi.org/10.1007/978-3-642-16438-5_33

Wang, Yiwen ; Wang, Shenghui ; Stash, Natalia et al. / Enhancing content-based recommendation with the task model of classification. Knowledge Engineering and Management by the Masses - 17th International Conference, EKAW 2010, Proceedings. 2010. pp. 431-440 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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 = "Yiwen Wang and Shenghui Wang and Natalia Stash and Lora Aroyo and Guus Schreiber",

year = "2010",

month = dec,

day = "22",

doi = "10.1007/978-3-642-16438-5_33",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "431--440",

booktitle = "Knowledge Engineering and Management by the Masses - 17th International Conference, EKAW 2010, Proceedings",

note = "17th International Conference on Knowledge Engineering and Management by the Masses, EKAW 2010, EKAW 2010 ; Conference date: 11-10-2010 Through 15-10-2010",

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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 Knowledge Engineering and Management by the Masses - 17th International Conference, EKAW 2010, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6317 LNAI, pp. 431-440, 17th International Conference on Knowledge Engineering and Management by the Masses, EKAW 2010, Lisbon, Portugal, 11/10/10. https://doi.org/10.1007/978-3-642-16438-5_33

Enhancing content-based recommendation with the task model of classification. / Wang, Yiwen; Wang, Shenghui; Stash, Natalia et al.
Knowledge Engineering and Management by the Masses - 17th International Conference, EKAW 2010, Proceedings. 2010. p. 431-440 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6317 LNAI).

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

TY - GEN

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

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AU - Wang, Shenghui

AU - Stash, Natalia

AU - Aroyo, Lora

AU - Schreiber, Guus

N1 - Conference code: 17

PY - 2010/12/22

Y1 - 2010/12/22

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.

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.

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

M3 - Conference contribution

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SN - 978-3-642-16437-8

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Wang Y, Wang S, Stash N, Aroyo L, Schreiber G. Enhancing content-based recommendation with the task model of classification. In Knowledge Engineering and Management by the Masses - 17th International Conference, EKAW 2010, Proceedings. 2010. p. 431-440. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-16438-5_33

Enhancing content-based recommendation with the task model of classification

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