Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 1
122-128

Capsule-based graph convolutional disentanglement for session-aware recommendation

Tao Yuhe1
Gao Rong1,2
Shao Xiongkai1
Wu Xinyun1
Li Jing3
1. School of Computer Science, Hubei University of Technology, Wuhan 430068, China
2. State Key Laboratory of New Computer Software Technology, Nanjing University, Nanjing 210093, China
3. School of Computer Science, Wuhan University, Wuhan 430068, China
DOI: 10.19734/j.issn.1001-3695.2022.05.0283

Abstract

To address the problem of poor recommendation accuracy in the session recommendation model, this paper proposed a capsule-based graph convolutional disentanglement for session-aware recommendation(CGCD) method. Specifically, it used disentangled learning technique to transition item embedding into factor embedding based on multiple sub-channels. Then, it used a capsule dynamic fusion strategy to aggregate different factors to obtain a new item embedding. In addition, it used a multi-head attention mechanism to assign weights to each item in the session. Finally, it aggregated the item embedding with other items in the current session according to the assigned weights, and then generated an accurate session representation to achieve item recommendation. Experiments on two publicly available real datasets show that the proposed model can improve the Pre@10, Pre@20, MRR@10 and MRR@20 by 5.17%, 2.99%, 6.56% and 2.94% on average of the recommended performance, which verifies the effectiveness and efficiency of the method in this paper.

Foundation Support

国家自然科学基金资助项目(61902116)
南京大学计算机软件新技术国家重点实验室开放课题(KFKT2021B12)
湖北省高层次人才基金资助项目(GCRC2020011)
湖北工业大学博士科研启动基金资助项目(BSQD2019026,BSQD2019022)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.05.0283
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 1
Section: Algorithm Research & Explore
Pages: 122-128
Serial Number: 1001-3695(2023)01-020-0122-07

Publish History

[2022-08-18] Accepted Paper
[2023-01-05] Printed Article

Cite This Article

陶玉合, 高榕, 邵雄凯, 等. 基于胶囊图卷积的解缠绕会话感知推荐方法 [J]. 计算机应用研究, 2023, 40 (1): 122-128. (Tao Yuhe, Gao Rong, Shao Xiongkai, et al. Capsule-based graph convolutional disentanglement for session-aware recommendation [J]. Application Research of Computers, 2023, 40 (1): 122-128. )

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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