App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 7

Adaptive modulation for multi-behavior recommendation using variational collective graph auto-encoder

Zhang Shuguang
Wang Yunlong
Zheng Xinyu
Cheng Yu
School of Electronics and Information Engineering, Anhui Jianzhu University, Hefei 230601, China
DOI: 10.19734/j.issn.1001-3695.2025.11.0461

Abstract

To address the limitations where existing multi-behavior recommendation methods fail to fully model personalized user preferences and overlook the differences in semantic intensity among behaviors, an Adaptive Modulation for Multi-Behavior Recommendation Using Variational Collective Graph Auto-encoder (AMVCGAE) is proposed. First, the model utilizes residual Graph Neural Networks and behavior-aware attention mechanisms to accurately capture complex dependencies among multiple behaviors. Second, it introduces an adaptive behavior modulation encoder, in which a gated enhancement network dynamically adapts behavior dimension weights and amplifies non-linear semantic differences to distinguish behavioral intensities for personalized modeling. Furthermore, the framework incorporates a dual-path decoupled generator during the variational inference stage, which constructs independent inference paths for mean and variance to prevent feature entanglement. Finally, the method adopts a multi-task joint optimization strategy. Experiments on three public datasets demonstrate that AMVCGAE significantly outperforms most baseline methods, improving HR@10 and NDCG@10 by an average of 12.28% and 7.7%, respectively. The proposed method effectively achieves refined personalized user modeling and distinguishes behavioral semantic intensities, significantly enhancing the accuracy and robustness of multi-behavior recommendation systems.

Foundation Support

安徽省科研创新平台数据共享技术与运行保障体系构建项目(GXXT-2021-093-2)
国家高等学校产学研创新基金新一代信息技术创新项目(2024IT129)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.11.0461
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 7

Publish History

[2026-03-25] Accepted Paper

Cite This Article

章曙光, 王云龙, 郑心雨, 等. 基于变分集体图自编码器的自适应调制多行为推荐方法 [J]. 计算机应用研究, 2026, 43 (7). (2026-03-27). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0461. (Zhang Shuguang, Wang Yunlong, Zheng Xinyu, et al. Adaptive modulation for multi-behavior recommendation using variational collective graph auto-encoder [J]. Application Research of Computers, 2026, 43 (7). (2026-03-27). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0461. )

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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