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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 7
2033-2038

Generative adversarial network recommendation based on reverse extension enhancement

Zhang Wenlong
Sun Fuzhen
Wu Xiangshuai
Li Pengcheng
Wang Shaoqing
School of Computer Science & Technology, Shandong University of Technology, Zibo Shandong 255049, China
DOI: 10.19734/j.issn.1001-3695.2023.11.0548

Abstract

Addressing the challenge of suboptimal performance in existing sequential recommendation models due to severe data sparsity, this paper proposed a generative adversarial network recommendation algorithm based on reverse extension enhancement. The approach extended and enhanced interaction sequences to obtain high-quality training data, mitigating the issue of insufficient model training caused by data sparsity. Firstly, it extended the project sequences backwardly using pseudo-prior terms to deepen the features of the project sequences. Secondly, it shifted the target of extension enhancement from short sequences to all user sequences, thoroughly exploring contextual information embedded in long sequences and alleviating noise issues arising from an excessively large proportion of pseudo-prior terms in augmented sequences. Finally, it employed a generative adversa-rial network with shared project embeddings, and jointly trained the discriminator and generator to enhance the model's recommendation performance. Experimental results on three public datasets demonstrate an average improvement of 30% in hit rate(HR@N) and normalized discounted cumulative gain(NDCG@N) compared to the optimal baseline ELECRec, confirming the effectiveness of reverse extension enhancement in mining sequence features and alleviating data sparsity.

Foundation Support

国家自然科学基金项目(61841602)
山东省自然科学基金项目(ZR2020MF147)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.11.0548
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 7
Section: Algorithm Research & Explore
Pages: 2033-2038
Serial Number: 1001-3695(2024)07-016-2033-06

Publish History

[2024-01-25] Accepted Paper
[2024-07-05] Printed Article

Cite This Article

张文龙, 孙福振, 吴相帅, 等. 基于反向延长增强的对抗生成网络推荐算法 [J]. 计算机应用研究, 2024, 41 (7): 2033-2038. (Zhang Wenlong, Sun Fuzhen, Wu Xiangshuai, et al. Generative adversarial network recommendation based on reverse extension enhancement [J]. Application Research of Computers, 2024, 41 (7): 2033-2038. )

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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