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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 1

Graph neural network recommendation model integrating dynamic neighborhood selection and multi-interest representation

Wang Chong
Gu Chengtong
Fu Xiang
Hong Xin
School of Business, Guilin University of Electronic Technology, Guilin Guangxi 541004, China
DOI: 10.19734/j.issn.1001-3695.2025.06.0185

Abstract

Recommendation systems often rely on graph neural networks to model complex interactions between users and items. However, existing methods generally adopt static or random neighborhood sampling strategies, which not only easily introduce noisy information but also fail to adapt to the dynamic changes of user interests. To address these issues, this paper proposed DNGM. On the user side, the model adopted a multi-head attention mechanism. It used multiple independent attention heads to focus on different feature subspaces in parallel. The model further captured users' multi-dimensional interest representations. On the item side, the model optimized the neighborhood selection strategy according to user interests and recommendation goals via the Actor-Critic reinforcement learning framework. It realized dynamic aggregation of neighborhood information, effectively suppressed noise interference, and improved representation quality. Experimental results on three public datasets, namely MovieLens-1M, Book-Crossing, and Last. FM, show that the proposed model outperforms existing mainstream models in terms of AUC, accuracy (ACC) , and other evaluation metrics. Specifically, AUC increases by up to 2.81% and ACC by up to 1.32%, demonstrating its effectiveness and robustness.

Foundation Support

国家自然科学基金资助项目(72061008)
广西自然科学基金资助项目(2018GXNSFAA294123)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.06.0185
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 1

Publish History

[2025-09-14] Accepted Paper

Cite This Article

王冲, 顾成通, 付翔, 等. 融合动态邻域选择与多兴趣建模的图神经网络推荐模型 [J]. 计算机应用研究, 2026, 43 (1). (2025-09-17). https://doi.org/10.19734/j.issn.1001-3695.2025.06.0185. (Wang Chong, Gu Chengtong, Fu Xiang, et al. Graph neural network recommendation model integrating dynamic neighborhood selection and multi-interest representation [J]. Application Research of Computers, 2026, 43 (1). (2025-09-17). https://doi.org/10.19734/j.issn.1001-3695.2025.06.0185. )

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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