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

TL;DR

This paper introduces a topic-aware knowledge graph approach leveraging large language models to improve recommender systems by extracting and refining general and specific topics, leading to better recommendation accuracy.

Contribution

It presents a novel method for extracting and refining topics from knowledge graphs using LLMs, enhancing interoperability and recommendation performance.

Findings

Significant improvement in recommendation accuracy across various knowledge graphs.

Effective extraction and refinement of general and specific topics using LLMs.

Enhanced understanding of item semantics and user preferences.

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.