RecKG: Knowledge Graph for Recommender Systems

Abstract:Knowledge graphs have proven successful in integrating heterogeneous data across various domains. However, there remains a noticeable dearth of research on their seamless integration among heterogeneous recommender systems, despite knowledge graph-based recommender systems garnering extensive research attention. This study aims to fill this gap by proposing RecKG, a standardized knowledge graph for recommender systems. RecKG ensures the consistent representation of entities across different datasets, accommodating diverse attribute types for effective data integration. Through a meticulous examination of various recommender system datasets, we select attributes for RecKG, ensuring standardized formatting through consistent naming conventions. By these characteristics, RecKG can seamlessly integrate heterogeneous data sources, enabling the discovery of additional semantic information within the integrated knowledge graph. We apply RecKG to standardize real-world datasets, subsequently developing an application for RecKG using a graph database. Finally, we validate RecKG's achievement in interoperability through a qualitative evaluation between RecKG and other studies.

Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)

arXiv:2501.03598 [cs.IR]

(or arXiv:2501.03598v2 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2501.03598 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1145/3605098.3636009 Focus to learn more DOI(s) linking to related resources

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