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.
Original language | English |
---|---|
Article number | 1304439 |
Number of pages | 19 |
Journal | Frontiers in Big Data |
Volume | 7 |
DOIs | |
Publication status | Published - 26 Feb 2024 |
Keywords
- Case-based recommendation
- Constraint solving
- Constraint-based recommendation
- Critiquing-based recommendation
- Knowledge-based recommender systems
- Model-based diagnosis
- Recommender systems
- Semantic recommender systems
- constraint solving
- semantic recommender systems
- model-based diagnosis
- recommender systems
- constraint-based recommendation
- knowledge-based recommender systems
- critiquing-based recommendation
- case-based recommendation
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Artificial Intelligence
- Information Systems