A Survey on Knowledge Graph-Based Recommender Systems
Dongze Li, Hanbing Qu, Jiaqiang Wang
Abstract
Recommender systems have emerged as indispensable tools for information filtering, and the integration of knowledge graphs for auxiliary information is becoming an increasingly popular research topic. This paper reviews recent studies, discussing the current state and practical applications of knowledge graph-based recommender systems. We summarize the strengths and weaknesses of various knowledge graph-based recommendation methods, noting that these systems significantly enhance performance in areas like accuracy, diversity, interpretability, and novelty. Furthermore, the trend of combining different knowledge graph-based methods underscores the mainstream evolution of recommender systems, warranting future exploration. We finish with an analysis of current challenges and a forward-looking perspective on future advances. This review aims to assist the reader in understanding and navigating this research field.