RecKG: Knowledge Graph for Recommender Systems

2024-04-08

ACM Symposium on Applied Computing

AI Summary

Data heterogeneity and semantic fragmentation across recommendation systems impede information fusion, hindering cross-domain collaboration and reproducibility. To address this, we propose RecKG—the first domain-specific knowledge graph standard for recommender systems—designed to enable standardized representation and interoperability of heterogeneous recommendation data. RecKG employs ontology-driven unified entity modeling, attribute normalization, and cross-dataset semantic alignment. We implement a queryable graph database using Neo4j and map/integrate major benchmark datasets, including MovieLens, Amazon, and Yelp. Experimental results demonstrate that RecKG significantly improves cross-source entity linking accuracy by +23.6%, supports semantic-aware model transfer, and facilitates interpretable recommendation analysis. By providing a reusable, extensible knowledge infrastructure, RecKG advances reproducible, collaborative research in recommender systems.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Knowledge Representation and Reasoning: Ontologies
• Application Domains: Humanities & Computational Social Science

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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.

Problem

• Information Fusion
• Recommendation Systems
• Interoperability

Innovation

• RecKG
• Unified Knowledge Graph
• Recommendation System Enhancement