Knowledge-based recommender systems: overview and research directions
Recommender systems are decision support systems that help users to identify items of relevance from a potentially large set of alternatives. In contrast to the mainstream recommendation approaches of collaborative filtering and content-based filtering, knowledge-based recommenders exploit semantic...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Big Data |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2024.1304439/full |
| _version_ | 1797295241349300224 |
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| author | Mathias Uta Alexander Felfernig Viet-Man Le Thi Ngoc Trang Tran Damian Garber Sebastian Lubos Tamim Burgstaller |
| author_facet | Mathias Uta Alexander Felfernig Viet-Man Le Thi Ngoc Trang Tran Damian Garber Sebastian Lubos Tamim Burgstaller |
| author_sort | Mathias Uta |
| collection | DOAJ |
| description | Recommender systems are decision support systems that help users to identify items of relevance from a potentially large set of alternatives. In contrast to the mainstream recommendation approaches of collaborative filtering and content-based filtering, knowledge-based recommenders exploit semantic user preference knowledge, item knowledge, and recommendation knowledge, to identify user-relevant items which is of specific relevance when dealing with complex and high-involvement items. Such recommenders are primarily applied in scenarios where users specify (and revise) their preferences, and related recommendations are determined on the basis of constraints or attribute-level similarity metrics. In this article, we provide an overview of the existing state-of-the-art in knowledge-based recommender systems. Different related recommendation techniques are explained on the basis of a working example from the domain of survey software services. On the basis of our analysis, we outline different directions for future research. |
| first_indexed | 2024-03-07T21:43:55Z |
| format | Article |
| id | doaj.art-daf85785e3d741faa2a0bd7467cd124e |
| institution | Directory Open Access Journal |
| issn | 2624-909X |
| language | English |
| last_indexed | 2024-03-07T21:43:55Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj.art-daf85785e3d741faa2a0bd7467cd124e2024-02-26T04:29:23ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2024-02-01710.3389/fdata.2024.13044391304439Knowledge-based recommender systems: overview and research directionsMathias Uta0Alexander Felfernig1Viet-Man Le2Thi Ngoc Trang Tran3Damian Garber4Sebastian Lubos5Tamim Burgstaller6Siemens Energy AG, Erlangen, GermanyInstitute of Software Technology (IST) - Applied Software Engineering & Ai Research Group (ASE), Graz University of Technology, Graz, AustriaInstitute of Software Technology (IST) - Applied Software Engineering & Ai Research Group (ASE), Graz University of Technology, Graz, AustriaInstitute of Software Technology (IST) - Applied Software Engineering & Ai Research Group (ASE), Graz University of Technology, Graz, AustriaInstitute of Software Technology (IST) - Applied Software Engineering & Ai Research Group (ASE), Graz University of Technology, Graz, AustriaInstitute of Software Technology (IST) - Applied Software Engineering & Ai Research Group (ASE), Graz University of Technology, Graz, AustriaInstitute of Software Technology (IST) - Applied Software Engineering & Ai Research Group (ASE), Graz University of Technology, Graz, AustriaRecommender systems are decision support systems that help users to identify items of relevance from a potentially large set of alternatives. In contrast to the mainstream recommendation approaches of collaborative filtering and content-based filtering, knowledge-based recommenders exploit semantic user preference knowledge, item knowledge, and recommendation knowledge, to identify user-relevant items which is of specific relevance when dealing with complex and high-involvement items. Such recommenders are primarily applied in scenarios where users specify (and revise) their preferences, and related recommendations are determined on the basis of constraints or attribute-level similarity metrics. In this article, we provide an overview of the existing state-of-the-art in knowledge-based recommender systems. Different related recommendation techniques are explained on the basis of a working example from the domain of survey software services. On the basis of our analysis, we outline different directions for future research.https://www.frontiersin.org/articles/10.3389/fdata.2024.1304439/fullrecommender systemssemantic recommender systemsknowledge-based recommender systemscase-based recommendationconstraint-based recommendationcritiquing-based recommendation |
| spellingShingle | Mathias Uta Alexander Felfernig Viet-Man Le Thi Ngoc Trang Tran Damian Garber Sebastian Lubos Tamim Burgstaller Knowledge-based recommender systems: overview and research directions Frontiers in Big Data recommender systems semantic recommender systems knowledge-based recommender systems case-based recommendation constraint-based recommendation critiquing-based recommendation |
| title | Knowledge-based recommender systems: overview and research directions |
| title_full | Knowledge-based recommender systems: overview and research directions |
| title_fullStr | Knowledge-based recommender systems: overview and research directions |
| title_full_unstemmed | Knowledge-based recommender systems: overview and research directions |
| title_short | Knowledge-based recommender systems: overview and research directions |
| title_sort | knowledge based recommender systems overview and research directions |
| topic | recommender systems semantic recommender systems knowledge-based recommender systems case-based recommendation constraint-based recommendation critiquing-based recommendation |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2024.1304439/full |
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