Integrating contrastive learning and adversarial learning on graph denoising encoder for recommendation

Wei Zhou

doi:10.1016/j.eswa.2024.125812

Integrating contrastive learning and adversarial learning on graph denoising encoder for recommendation

2025-03-01

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DOI：10.1016/j.eswa.2024.125812

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In this era of information explosion, recommender systems have become increasingly vital in research and everyday applications. Collaborative filtering and matrix factorization are foundational techniques in this domain, while they encounter limitations in capturing complex user behavior and managing sparse data. Graph Convolutional Networks (GCNs) have emerged as a superior alternative. However, challenges for GCNs remain, particularly regarding data sparsity and interaction noise. We propose a Contrastive Adversarial Graph Denoising Encoder (CAGDE), which conducts contrastive learning and adversarial training into a Graph Denoising Encoder (GDE) framework to address these issues. This approach eliminates the necessity for neighborhood aggregation to address the over-smoothing problem and focuses on essential graph features by adding different types of noise to enhance robustness. Extensive experiments were conducted on five benchmark datasets, and the proposed solution, CAGDE, demonstrates significant improvements in recommendation accuracy and robustness against noise. Our implementations are available at https://github.com/Azunyan-bot/CAGDE.

Keywords:

Recommender systems

Contrastive learning

Collaborative filtering

Adversarial learning

Integrating contrastive learning and adversarial learning on graph denoising encoder for recommendation

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