App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 8

Hypergraph-augmented GCN meets SVD-enhanced graph contrastive learning for recommendation

Wang Hui
Liu Junyan
You Junjie
Huang Runjie
Jiangxi University of Science and Technology, School of Information Engineering, Jiangxi Ganzhou 341000, China
DOI: 10.19734/j.issn.1001-3695.2026.01.0001

Abstract

Graph neural networks (GNNs) are widely used in collaborative filtering (CF) for recommendation systems to model user-item interactions, but their performance drops significantly under sparse and noisy user-item data in practical applications. A new recommendation model called hypergraph-augmented GCN meets SVD-enhanced graph contrastive learning for recommendation (HSVDGCL) was proposed to solve these problems. The model adopt a bipartite graph architecture strengthened by hypergraph connections and singular value decomposition (SVD) . It built two separate graph views: one modeled high-order relationships with hypergraphs, and the other captured global collaborative patterns via SVD. Cross-view contrastive learning between the two views combined local interaction signals and global structural information, and enhanced the model robustness to data sparsity and noise. Experiments on Yelp, Gowalla, Amazon and Tmall datasets show that the model improves Recall and NDCG by at least 3.22%, 13.37% and 20.69% respectively. Ablation experiments confirm the effectiveness of each module, and the model delivers better recommendation performance than existing advanced baseline models.

Foundation Support

国家自然科学基金资助项目(62366016)
江西省教育厅科技项目(GJJ2200839)
江西理工大学博士启动专项(205200100659)
江西省教改课题(JXJG-24-7-17)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.01.0001
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 8

Publish History

[2026-04-22] Accepted Paper

Cite This Article

王慧, 刘俊琰, 游俊杰, 等. 基于超图和奇异值分解对比学习的推荐系统 [J]. 计算机应用研究, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2026.01.0001. (Wang Hui, Liu Junyan, You Junjie, et al. Hypergraph-augmented GCN meets SVD-enhanced graph contrastive learning for recommendation [J]. Application Research of Computers, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2026.01.0001. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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