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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 5

Graph neural recommendation algorithm with implicit relationship enhancement

Xiong Zhongmin
Zhang Jun
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
DOI: 10.19734/j.issn.1001-3695.2024.09.0363

Abstract

In recommendation tasks, user social network information and user-item interaction information can be used to enhance recommendation performance. However, existing social recommendation algorithms often rely only on the initial social and interaction graphs, failing to fully explore potential link relationships between users and items and overlooking the unreliability of social relationships. To address these issues, this paper proposes a graph neural network-based recommendation algorithm that integrates implicit relationships (IREGraphRec) . First, the algorithm extracts potential information from multiple perspectives to obtain reliable user social and user-item interaction information. It reconstructs this information into a heterogeneous information network based on user preferences. Feature vector representations are generated using graph embeddings and multiple defined meta-paths, while an attention mechanism assigns different weights during the information aggregation process. Finally, the model performs multiple rounds of learning within the graph neural network to achieve the final prediction results. We evaluated the proposed model on three public datasets, including Epinions, and compared it with traditional network models such as S4Rec. The results showed a 1.65% reduction in MAE and a 2.34% reduction in RMSE. Experimental results and analysis demonstrate the superior performance of our model.

Foundation Support

广东省重点领域研发计划项目(2021B0202070001)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.09.0363
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 5

Publish History

[2025-01-20] Accepted Paper

Cite This Article

熊中敏, 张军. 隐式关系增强的图神经网络推荐算法 [J]. 计算机应用研究, 2025, 42 (5). (2025-01-24). https://doi.org/10.19734/j.issn.1001-3695.2024.09.0363. (Xiong Zhongmin, Zhang Jun. Graph neural recommendation algorithm with implicit relationship enhancement [J]. Application Research of Computers, 2025, 42 (5). (2025-01-24). https://doi.org/10.19734/j.issn.1001-3695.2024.09.0363. )

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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