RecKG: Knowledge Graph for Recommender Systems
This work addresses data integration challenges for researchers and practitioners building knowledge graph-based recommender systems, though it appears incremental as it standardizes existing approaches rather than introducing new methods.
The authors tackled the problem of inconsistent knowledge graph representations across heterogeneous recommender systems by proposing RecKG, a standardized knowledge graph that ensures consistent entity representation and data integration. They validated RecKG's interoperability through qualitative evaluation against other studies.
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.