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LoRA-NCL: Neighborhood-Enriched Contrastive Learning with Low-Rank Dimensionality Reduction for Graph Collaborative Filtering

Author

Listed:
  • Tianruo Cao

    (Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China)

  • Honghui Chen

    (Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China)

  • Zepeng Hao

    (Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China)

  • Tao Hu

    (Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China)

Abstract

Graph Collaborative Filtering (GCF) methods have emerged as an effective recommendation approach, capturing users’ preferences over items by modeling user–item interaction graphs. However, these methods suffer from data sparsity in real scenarios, and their performance can be improved using contrastive learning. In this paper, we propose an optimized method, named LoRA-NCL, for GCF based on Neighborhood-enriched Contrastive Learning (NCL) and low-rank dimensionality reduction. We incorporate low-rank features obtained through matrix factorization into the NCL framework and employ LightGCN to extract high-dimensional representations. Extensive experiments on five public datasets demonstrate that the proposed method outperforms a competitive graph collaborative filtering base model, achieving 4.6% performance gains on the MovieLens dataset, respectively.

Suggested Citation

  • Tianruo Cao & Honghui Chen & Zepeng Hao & Tao Hu, 2023. "LoRA-NCL: Neighborhood-Enriched Contrastive Learning with Low-Rank Dimensionality Reduction for Graph Collaborative Filtering," Mathematics, MDPI, vol. 11(16), pages 1-13, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3577-:d:1219984
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