TY - JOUR
T1 - Knowledge-based recommender systems
T2 - overview and research directions
AU - Uta, M
AU - Felfernig, A
AU - Le, VM
AU - Tran, TNT
AU - Garber, D
AU - Lubos, S
AU - Burgstaller, T
PY - 2024/2/26
Y1 - 2024/2/26
N2 - 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.
AB - 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.
KW - Case-based recommendation
KW - Constraint solving
KW - Constraint-based recommendation
KW - Critiquing-based recommendation
KW - Knowledge-based recommender systems
KW - Model-based diagnosis
KW - Recommender systems
KW - Semantic recommender systems
KW - constraint solving
KW - semantic recommender systems
KW - model-based diagnosis
KW - recommender systems
KW - constraint-based recommendation
KW - knowledge-based recommender systems
KW - critiquing-based recommendation
KW - case-based recommendation
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=pure-test&SrcAuth=WosAPI&KeyUT=WOS:001178287500001&DestLinkType=FullRecord&DestApp=WOS_CPL
UR - http://www.scopus.com/inward/record.url?scp=85187152419&partnerID=8YFLogxK
U2 - 10.3389/fdata.2024.1304439
DO - 10.3389/fdata.2024.1304439
M3 - Review article
C2 - 38469430
SN - 2624-909X
VL - 7
JO - Frontiers in Big Data
JF - Frontiers in Big Data
M1 - 1304439
ER -