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

Tianruo Cao, Honghui Chen (), Zepeng Hao and Tao Hu
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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

Mathematics, 2023, vol. 11, issue 16, 1-13

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.

Keywords: Contrastive Learning; low-rank dimensionlity reduction; Graph Collaborative Filtering (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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