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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 8

Time-block-based dynamic graph neural network for sequential recommendation

Peng Zihang1
Zhang Quangui2
Jin Haibo1
Liu Yixin1
Qi Yuxin1
1. School of Software, Liaoning Technical University, Huludao Liaoning 125000, China
2. School of Mathematics & Artificial Intelligence, Chongqing University of Arts & Sciences, Yongchuan, Chongqing 402160, China
DOI: 10.19734/j.issn.1001-3695.2024.12.0518

Abstract

Dynamic graph-based sequential recommendation is a current research hotspot in the recommendation system domain. Existing methods typically constructed dynamic graphs at each timestamp of user-item interaction sequences, which struggled to address noise caused by occasional user behaviors and failed to effectively capture users' periodic preferences. To address these challenges, this paper proposed a Time-Block-Based Dynamic Graph Neural Network (TBDGNN) for sequential recommendation. The method first divided temporal sequences into multiple time blocks based on the distribution of user-item interaction history data. It then constructed dynamic graphs within each time block to model temporal evolution of user behaviors. Additionally, a time-block-level graph neural network framework was designed to mitigate the impact of occasional interactions (e. g. , user misoperations) and capture users' periodic behaviors through time-block partitioning. Experimental results demonstrated that TBDGNN significantly outperformed the DGEL baseline on core metrics in datasets including Movielens, achieving a maximum improvement of 8.7% in Hit@10. The findings validate the model's effectiveness in dynamic recommendation and periodic behavior modeling.

Foundation Support

国家自然科学基金资助项目(62173171)
重庆市教委科技项目-重点项目(KJZD-K202301309)
重庆文理学院塔基计划(R2021FRG01)
重庆文理学院塔尖计划项目(P2022RG08)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.12.0518
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 8

Publish History

[2025-03-25] Accepted Paper

Cite This Article

彭梓航, 张全贵, 金海波, 等. 基于时间块动态图神经网络的序列推荐方法 [J]. 计算机应用研究, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2024.12.0518. (Peng Zihang, Zhang Quangui, Jin Haibo, et al. Time-block-based dynamic graph neural network for sequential recommendation [J]. Application Research of Computers, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2024.12.0518. )

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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