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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 7

Sequence recommendation model based on hypergraphs and hierarchical spectral filters

Zhu Wenqiang
Zhu Rongrong
School of Software & Internet of Things Engineering, Jiangxi University of Finance & Economics, Nanchang 330032, China
DOI: 10.19734/j.issn.1001-3695.2024.12.0488

Abstract

To address the data sparsity problem prevalent in sequence recommendation and the noise issue caused by unexpected interactions between users and items, we propose a sequence recommendation model based on hypergraph and hierarchical spectral filters (HYFTRec) . HYFTRec introduces the hypergraph structure into sequence recommendation, captures the complex higher-order relationships between users and items through the hypergraph embedding module, and at the same time utilizes the hierarchical spectral filter for effective denoising in the frequency domain for effective denoising, which improves the accuracy and robustness of recommendation. In addition, the model incorporates a comparative learning framework to optimize the characterization ability of user behavior sequences. Through multiple experimental validations on three public datasets, HYFTRec demonstrates advantages in terms of key metrics hit rate (HR) and normalized discount cumulative gain (NDCG) , which significantly outperforms existing sequence recommendation models. Compared with the benchmark model FMLP, HYFTRec improves 10.7%, 10.8%, and 7.6% in HR@10 metrics and 12.5%, 13.7%, and 7.3% in NDCG@10 metrics, respectively. These results verify the validity and superiority of the proposed model.

Foundation Support

国家自然科学基金资助项目(72261016)
江西省教育厅科技项目(GJJ200515)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.12.0488
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 7

Publish History

[2025-03-13] Accepted Paper

Cite This Article

朱文强, 朱蓉蓉. 基于超图和分层频谱滤波器的序列推荐模型 [J]. 计算机应用研究, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.12.0488. (Zhu Wenqiang, Zhu Rongrong. Sequence recommendation model based on hypergraphs and hierarchical spectral filters [J]. Application Research of Computers, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.12.0488. )

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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