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

Knowledge graph convolutional network recommendation method with Rényi differential privacy

Wang Shiyuan1,2
Jia Dan1,2
Zhang Xing1,2
1. Key Laboratory of Security for Network and Data in Industrial Internet of Liaoning Province, Jinzhou Liaoning 121001, China
2. School of Electronics & Information Engineering, Liaoning University of Technology, Jinzhou Liaoning 121001, China
DOI: 10.19734/j.issn.1001-3695.2025.12.0506

Abstract

To address data sparsity, cold start, under-utilization of auxiliary information, and privacy leakage in knowledge graph convolutional network (KGCN) recommendation systems, this paper proposes a Knowledge Graph Convolutional Network Recommendation Method with Rényi Differential Privacy (KGRRDP) . First, it constructs interactive user-item preference embeddings to mine deep associations, integrates auxiliary information via multi-hop neighbor aggregation, and dynamically assigns feature weights using an adaptive attention mechanism. This improves recommendation accuracy and interpretability, effectively solving the issues of data sparsity, cold start, and under-utilization of auxiliary information. Second, it applies random perturbations to embedding vectors for privacy noise injection based on the Rényi Differential Privacy (RDP) framework, enabling more accurate privacy loss quantification. A gradient dynamic update strategy optimizes sensitivity computation, enhancing privacy protection while reducing model accuracy loss. Experimental results show that KGRRDP outperforms the best baseline, achieving AUC improvements of 1.8%–3.9% and F1 score improvements of 2.5%–3.2% across multiple datasets. Moreover, it exhibits low sensitivity to privacy budget variations, striking an effective balance between privacy protection and recommendation performance.

Foundation Support

辽宁省科技计划联合计划(重点研发计划项目)(2025JH2/101800231)
辽宁省属本科高校基本科研业务费专项资金(LJZZ232410154013)
辽宁省教育厅科学研究经费项目(JYTMS20230838)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.12.0506
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 8

Publish History

[2026-04-22] Accepted Paper

Cite This Article

王诗源, 贾丹, 张兴. 融合Rényi差分隐私的知识图卷积网络推荐方法 [J]. 计算机应用研究, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2025.12.0506. (Wang Shiyuan, Jia Dan, Zhang Xing. Knowledge graph convolutional network recommendation method with Rényi differential privacy [J]. Application Research of Computers, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2025.12.0506. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)