An Enhanced Semantic Layer for Hybrid Recommender Systems : Application to News Recommendation
Recommender systems have achieved success in a variety of domains, as a means to help users in information overload scenarios by proactively finding items or services on their behalf, taking into account or predicting their tastes, priorities, or goals. Challenging issues in their research agenda in...
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| Autor*in: |
Cantador, Iván [verfasserIn] Castells, Pablo [author] Bellogín, Alejandro [author] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2011 |
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| Schlagwörter: |
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| Umfang: |
Online-Ressource |
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| Reproduktion: |
IGI Global InfoSci Journals Archive 2000 - 2012 |
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In: International journal on semantic web and information systems - Hershey,PA : IGI Global, 2005, 7(2011), 1, Seite 44-78 |
| Übergeordnetes Werk: |
volume:7 ; year:2011 ; number:1 ; pages:44-78 |
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10.4018/jswis.2011010103 |
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| 520 | |a Recommender systems have achieved success in a variety of domains, as a means to help users in information overload scenarios by proactively finding items or services on their behalf, taking into account or predicting their tastes, priorities, or goals. Challenging issues in their research agenda include the sparsity of user preference data and the lack of flexibility to incorporate contextual factors in the recommendation methods. To a significant extent, these issues can be related to a limited description and exploitation of the semantics underlying both user and item representations. The authors propose a three-fold knowledge representation, in which an explicit, semantic-rich domain knowledge space is incorporated between user and item spaces. The enhanced semantics support the development of contextualisation capabilities and enable performance improvements in recommendation methods. As a proof of concept and evaluation testbed, the approach is evaluated through its implementation in a news recommender system, in which it is tested with real users. In such scenario, semantic knowledge bases and item annotations are automatically produced from public sources | ||
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10.4018/jswis.2011010103 doi (DE-627)NLEJ24450444X (VZGNL)10.4018/jswis.2011010103 DE-627 ger DE-627 rakwb eng Cantador, Iván verfasserin aut An Enhanced Semantic Layer for Hybrid Recommender Systems Application to News Recommendation 2011 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Recommender systems have achieved success in a variety of domains, as a means to help users in information overload scenarios by proactively finding items or services on their behalf, taking into account or predicting their tastes, priorities, or goals. Challenging issues in their research agenda include the sparsity of user preference data and the lack of flexibility to incorporate contextual factors in the recommendation methods. To a significant extent, these issues can be related to a limited description and exploitation of the semantics underlying both user and item representations. The authors propose a three-fold knowledge representation, in which an explicit, semantic-rich domain knowledge space is incorporated between user and item spaces. The enhanced semantics support the development of contextualisation capabilities and enable performance improvements in recommendation methods. As a proof of concept and evaluation testbed, the approach is evaluated through its implementation in a news recommender system, in which it is tested with real users. In such scenario, semantic knowledge bases and item annotations are automatically produced from public sources IGI Global InfoSci Journals Archive 2000 - 2012 Collaborative Filtering Context Modelling Domain Knowledge Ontologies Recommender Systems Semantics Castells, Pablo author aut Bellogín, Alejandro author aut In International journal on semantic web and information systems Hershey,PA : IGI Global, 2005 7(2011), 1, Seite 44-78 Online-Ressource (DE-627)NLEJ244419582 (DE-600)2401011-X 1552-6291 nnns volume:7 year:2011 number:1 pages:44-78 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jswis.2011010103 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jswis.2011010103&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 7 2011 1 44-78 |
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10.4018/jswis.2011010103 doi (DE-627)NLEJ24450444X (VZGNL)10.4018/jswis.2011010103 DE-627 ger DE-627 rakwb eng Cantador, Iván verfasserin aut An Enhanced Semantic Layer for Hybrid Recommender Systems Application to News Recommendation 2011 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Recommender systems have achieved success in a variety of domains, as a means to help users in information overload scenarios by proactively finding items or services on their behalf, taking into account or predicting their tastes, priorities, or goals. Challenging issues in their research agenda include the sparsity of user preference data and the lack of flexibility to incorporate contextual factors in the recommendation methods. To a significant extent, these issues can be related to a limited description and exploitation of the semantics underlying both user and item representations. The authors propose a three-fold knowledge representation, in which an explicit, semantic-rich domain knowledge space is incorporated between user and item spaces. The enhanced semantics support the development of contextualisation capabilities and enable performance improvements in recommendation methods. As a proof of concept and evaluation testbed, the approach is evaluated through its implementation in a news recommender system, in which it is tested with real users. In such scenario, semantic knowledge bases and item annotations are automatically produced from public sources IGI Global InfoSci Journals Archive 2000 - 2012 Collaborative Filtering Context Modelling Domain Knowledge Ontologies Recommender Systems Semantics Castells, Pablo author aut Bellogín, Alejandro author aut In International journal on semantic web and information systems Hershey,PA : IGI Global, 2005 7(2011), 1, Seite 44-78 Online-Ressource (DE-627)NLEJ244419582 (DE-600)2401011-X 1552-6291 nnns volume:7 year:2011 number:1 pages:44-78 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jswis.2011010103 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jswis.2011010103&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 7 2011 1 44-78 |
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Recommender systems have achieved success in a variety of domains, as a means to help users in information overload scenarios by proactively finding items or services on their behalf, taking into account or predicting their tastes, priorities, or goals. Challenging issues in their research agenda include the sparsity of user preference data and the lack of flexibility to incorporate contextual factors in the recommendation methods. To a significant extent, these issues can be related to a limited description and exploitation of the semantics underlying both user and item representations. The authors propose a three-fold knowledge representation, in which an explicit, semantic-rich domain knowledge space is incorporated between user and item spaces. The enhanced semantics support the development of contextualisation capabilities and enable performance improvements in recommendation methods. As a proof of concept and evaluation testbed, the approach is evaluated through its implementation in a news recommender system, in which it is tested with real users. In such scenario, semantic knowledge bases and item annotations are automatically produced from public sources |
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Recommender systems have achieved success in a variety of domains, as a means to help users in information overload scenarios by proactively finding items or services on their behalf, taking into account or predicting their tastes, priorities, or goals. Challenging issues in their research agenda include the sparsity of user preference data and the lack of flexibility to incorporate contextual factors in the recommendation methods. To a significant extent, these issues can be related to a limited description and exploitation of the semantics underlying both user and item representations. The authors propose a three-fold knowledge representation, in which an explicit, semantic-rich domain knowledge space is incorporated between user and item spaces. The enhanced semantics support the development of contextualisation capabilities and enable performance improvements in recommendation methods. As a proof of concept and evaluation testbed, the approach is evaluated through its implementation in a news recommender system, in which it is tested with real users. In such scenario, semantic knowledge bases and item annotations are automatically produced from public sources |
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Recommender systems have achieved success in a variety of domains, as a means to help users in information overload scenarios by proactively finding items or services on their behalf, taking into account or predicting their tastes, priorities, or goals. Challenging issues in their research agenda include the sparsity of user preference data and the lack of flexibility to incorporate contextual factors in the recommendation methods. To a significant extent, these issues can be related to a limited description and exploitation of the semantics underlying both user and item representations. The authors propose a three-fold knowledge representation, in which an explicit, semantic-rich domain knowledge space is incorporated between user and item spaces. The enhanced semantics support the development of contextualisation capabilities and enable performance improvements in recommendation methods. As a proof of concept and evaluation testbed, the approach is evaluated through its implementation in a news recommender system, in which it is tested with real users. In such scenario, semantic knowledge bases and item annotations are automatically produced from public sources |
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An Enhanced Semantic Layer for Hybrid Recommender Systems |
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Castells, Pablo Bellogín, Alejandro |
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