Topic-Aware Knowledge Graph with Large Language Models for Interoperability in Recommender Systems

Abstract

The use of knowledge graphs in recommender systems has become one of the common approaches to addressing data sparsity and cold start problems. Recent advances in large language models (LLMs) offer new possibilities for processing side and context information within knowledge graphs. However, consistent integration across various systems remains challenging due to the need for domain expert intervention and differences in system characteristics. To address these issues, we propose a consistent approach that extracts both general and specific topics from both side and context information using LLMs. First, general topics are iteratively extracted and updated from side information. Then, specific topics are extracted using context information. Finally, to address synonymous topics generated during the specific topic extraction process, a refining algorithm processes and resolves these issues effectively. This approach allows general topics to capture broad knowledge across diverse item characteristics, while specific topics emphasize detailed attributes, providing a more comprehensive understanding of the semantic features of items and the preferences of users. Experimental results demonstrate significant improvements in recommendation performance across diverse knowledge graphs.

Accepted by The 40th ACM/SIGAPP Symposium On Applied Computing(SAC) 2025

References in corpus (5)

• KGAT: Knowledge Graph Attention Network for Recommendation
• RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
• Learning Intents behind Interactions with Knowledge Graph for Recommendation
• Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
• RecKG: Knowledge Graph for Recommender Systems