Boosting Knowledge Graph-based Recommendations through Confidence-Aware Augmentation with Large Language Models

TL;DR

This paper introduces CKG-LLMA, a framework that enhances knowledge graph-based recommendations by integrating large language models with confidence-aware augmentation, filtering, and contrastive learning to improve recommendation accuracy and explanation quality.

Contribution

The paper presents a novel framework combining LLMs with KGs for recommendations, including new augmentation, filtering, and learning techniques to address noise and improve relevance.

Findings

Significant improvement in recommendation accuracy across multiple datasets.

Effective filtering of noisy KG triplets using confidence-aware mechanisms.

Enhanced explanation quality guided by confidence-aware generation.

Abstract

Knowledge Graph-based recommendations have gained significant attention due to their ability to leverage rich semantic relationships. However, constructing and maintaining Knowledge Graphs (KGs) is resource-intensive, and the accuracy of KGs can suffer from noisy, outdated, or irrelevant triplets. Recent advancements in Large Language Models (LLMs) offer a promising way to improve the quality and relevance of KGs for recommendation tasks. Despite this, integrating LLMs into KG-based systems presents challenges, such as efficiently augmenting KGs, addressing hallucinations, and developing effective joint learning methods. In this paper, we propose the Confidence-aware KG-based Recommendation Framework with LLM Augmentation (CKG-LLMA), a novel framework that combines KGs and LLMs for recommendation task. The framework includes: (1) an LLM-based subgraph augmenter for enriching KGs with high-quality information, (2) a confidence-aware message propagation mechanism to filter noisy triplets, and (3) a dual-view contrastive learning method to integrate user-item interactions and KG data. Additionally, we employ a confidence-aware explanation generation process to guide LLMs in producing realistic explanations for recommendations. Finally, extensive experiments demonstrate the effectiveness of CKG-LLMA across multiple public datasets.