Knowledge-based recommender systems: overview and research directions - Uta, Mathias; Felfernig, Alexander; Le, Viet-Man; Tran, Thi Ngoc Trang; Garber, Damian; Lubos, Sebastian; Burgstaller, Tamim (Frontiers in Big Data, 2024)
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, knowledgebased 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.