App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 9

Graph recommendation model integrating multi-scale causal debiasing and self-supervised adversarial purification

Yang Yonghe1
Xu Yang1
Ping Peng2
1. School of Computer Science, Nanjing University of Information Science &Technology, Nanjing Jiangsu 210000, China
2. School of Transportation and Civil Engineering, Nantong University, Nantong Jiangsu 226000, China
DOI: 10.19734/j.issn.1001-3695.2026.02.0006

Abstract

In graph recommendation systems, user interactions are frequently disturbed by confounding factors such as popularity trends and community effects, which distort authentic user preference features and degrade long-tail item recall performance. To tackle such challenges, this study proposes a graph recommendation model named MCAP that combines multi-scale causal debiasing and self-supervised adversarial purification. The model constructs a parallel dual-branch graph convolutional structure, independently extracts user interest embeddings from user-item interaction graph and bias embeddings from bias correlation graph, and separates the two representations via decoupling loss. On this basis, a multi-scale causal debiasing chain is established to conduct causal intervention on interest embeddings according to bias information at embedding level, and suppress bias amplification at scoring level with global topological features of bias association graph. A self-supervised adversarial discrimination module is further introduced to eliminate residual confounding interference. Gradient reversal layer is adopted to realize adversarial training between interest branch and discriminator, and improve the purity of unbiased preference representations. Extensive experiments on MovieLens-10M and Netflix datasets demonstrate that MCAP achieves superior Recall@20/50 and NDCG@20/50 performance compared with mainstream debiasing recommendation baselines. The results verify that the proposed method effectively alleviates confounding interference and boosts overall recommendation quality.

Foundation Support

国家自然科学基金资助项目(52202496)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.02.0006
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 9

Publish History

[2026-05-20] Accepted Paper

Cite This Article

杨咏荷, 徐扬, 平鹏. 融合多尺度因果去偏与自监督对抗判别的图推荐模型 [J]. 计算机应用研究, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2026.02.0006. (Yang Yonghe, Xu Yang, Ping Peng. Graph recommendation model integrating multi-scale causal debiasing and self-supervised adversarial purification [J]. Application Research of Computers, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2026.02.0006. )

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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