AUTHOR=Uta Mathias , Felfernig Alexander , Le Viet-Man , Tran Thi Ngoc Trang , Garber Damian , Lubos Sebastian , Burgstaller Tamim TITLE=Knowledge-based recommender systems: overview and research directions JOURNAL=Frontiers in Big Data VOLUME=7 YEAR=2024 URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1304439 DOI=10.3389/fdata.2024.1304439 ISSN=2624-909X ABSTRACT=

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.