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Enhanced knowledge graph recommendation algorithm based on multi-level contrastive learning.

Zhang Rong1, Liu Yuan2, Li Yang3

  • 1School of Internet of Things Engineering, Jiangsu Vocational College of Information Technology, Wuxi, 214153, China. 95291188@qq.com.

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|October 4, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for knowledge graph-based recommendation systems, using multi-level contrastive learning to overcome data sparsity and improve personalization accuracy.

Keywords:
Contrastive learningGraph Neural Network (GNN)Knowledge graphKnowledge-aware recommendationRecommendation system

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Data Science

Background:

  • Knowledge Graphs (KGs) improve recommendation systems but suffer from data sparsity due to long-tail distributions.
  • Existing methods struggle to effectively leverage KG information for personalized recommendations.

Purpose of the Study:

  • To propose a knowledge-aware recommendation framework addressing data sparsity in KGs.
  • To enhance recommendation accuracy and personalization through multi-level contrastive learning.

Main Methods:

  • A Collaborative Knowledge Graph (CKG) is enhanced using random edge dropout for multi-level feature representation (user-user, item-item, user-item).
  • Graph Attention Networks (GAT) with a dynamic attention mechanism model the KG.
  • Multi-level contrastive learning, incorporating nonlinear transformation and Momentum Contrast (Moco), is used as an auxiliary self-supervised task.

Main Results:

  • The framework effectively extracts high-quality feature information from the KG.
  • Experimental results on MovieLens and Amazon-books datasets show significant improvements in recommendation performance.
  • The proposed method outperforms baseline models across various evaluation metrics.

Conclusions:

  • The knowledge-aware recommendation framework successfully mitigates data sparsity issues in KGs.
  • Multi-level contrastive learning enhances the effectiveness of KG-based recommendation systems.
  • The approach offers a robust solution for personalized recommendations in sparse data environments.