{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T17:38:52Z","timestamp":1774719532682,"version":"3.50.1"},"reference-count":130,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008332","name":"TU Graz, Internationale Beziehungen und Mobilit\u00e4tsprogramme","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008332","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Big Data"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3389\/fdata.2024.1304439","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T04:29:32Z","timestamp":1708921772000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["Knowledge-based recommender systems: overview and research directions"],"prefix":"10.3389","volume":"7","author":[{"given":"Mathias","family":"Uta","sequence":"first","affiliation":[]},{"given":"Alexander","family":"Felfernig","sequence":"additional","affiliation":[]},{"given":"Viet-Man","family":"Le","sequence":"additional","affiliation":[]},{"given":"Thi Ngoc Trang","family":"Tran","sequence":"additional","affiliation":[]},{"given":"Damian","family":"Garber","sequence":"additional","affiliation":[]},{"given":"Sebastian","family":"Lubos","sequence":"additional","affiliation":[]},{"given":"Tamim","family":"Burgstaller","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,2,26]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-29659-3","author":"Aggarwal","year":"2016","journal-title":"Recommender Systems: The Textbook, 1st Edn."},{"key":"B2","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1504\/IJCISTUDIES.2018.094894","article-title":"A constraint-based job recommender system integrating fodra","volume":"7","author":"Almalis","year":"2018","journal-title":"Int. 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