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

Graph denoising and adding model with graph contrastive learning for social recommendation

Song Xiujie
Ma Tao
Chen Qiaoyu
Bai Xue
School of Mathematics and Computer Science, Ningxia Normal University, Guyuan Ningxia 756000, China
DOI: 10.19734/j.issn.1001-3695.2025.12.0512

Abstract

To address the issue that redundant social relationships in social recommender systems interfere with user representation learning and degrade recommendation performance, this study proposed a graph denoising and augmentation model for social recommendation based on graph contrastive learning (GDA_GCL) . The model examined the impact of noise on recommendation performance from both denoising and augmentation perspectives. On the one hand, it introduced user preference–guided social graph denoising to mitigate the negative influence of redundant social relationships on recommendation results. On the other hand, it injected noise into the social graph to simulate the existence of real-world social noise, thereby enhancing the robustness of the model. In addition, the study designed a self-supervised learning module to improve the consistency of learned representations. Experimental results on two public datasets demonstrated that, compared with the best-performing baseline methods, the proposed model improved Recall@20 by 3.48% and 5.93%, and increased NDCG@20 by 4.41% and 5.99%, respectively. These results indicate that the proposed model effectively alleviates the interference caused by social noise and significantly enhances the performance of social recommendation.

Foundation Support

国家自然科学基金项目(62262054)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.12.0512
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 8

Publish History

[2026-04-22] Accepted Paper

Cite This Article

宋秀杰, 马涛, 陈巧玉, 等. 基于图对比学习的社交推荐图去噪与增强模型 [J]. 计算机应用研究, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2025.12.0512. (Song Xiujie, Ma Tao, Chen Qiaoyu, et al. Graph denoising and adding model with graph contrastive learning for social recommendation [J]. Application Research of Computers, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2025.12.0512. )

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.

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