Comprehending Knowledge Graphs with Large Language Models for Recommender Systems

TL;DR

This paper introduces CoLaKG, a novel approach leveraging large language models to enhance knowledge graph-based recommender systems by addressing missing data, semantic loss, and high-order connection challenges.

Contribution

It proposes a new method that uses LLMs for supplementing missing facts and better utilizing semantic information in KGs for improved recommendations.

Findings

Outperforms existing methods on four real-world datasets.

Effectively captures local and global KG information.

Enhances recommendation accuracy through semantic enrichment.

Abstract

In recent years, the introduction of knowledge graphs (KGs) has significantly advanced recommender systems by facilitating the discovery of potential associations between items. However, existing methods still face several limitations. First, most KGs suffer from missing facts or limited scopes. Second, existing methods convert textual information in KGs into IDs, resulting in the loss of natural semantic connections between different items. Third, existing methods struggle to capture high-order connections in the global KG. To address these limitations, we propose a novel method called CoLaKG, which leverages large language models (LLMs) to improve KG-based recommendations. The extensive knowledge and remarkable reasoning capabilities of LLMs enable our method to supplement missing facts in KGs, and their powerful text understanding abilities allow for better utilization of semantic information. Specifically, CoLaKG extracts useful information from KGs at both local and global levels. By employing the item-centered subgraph extraction and prompt engineering, it can accurately understand the local information. In addition, through the semantic-based retrieval module, each item is enriched by related items from the entire knowledge graph, effectively harnessing global information. Furthermore, the local and global information are effectively integrated into the recommendation model through a representation fusion module and a retrieval-augmented representation learning module, respectively. Extensive experiments on four real-world datasets demonstrate the superiority of our method.