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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 6
1760-1766

Collaborative filtering recommendation algorithm based on adaptive neural graph convolution attention neural network

Du Yuxuan1a,1b
Wang Wei1a,1b,2
Zhang Chuang1a,1b
Zheng Xiaoli1a,1b
Su Jiatao1a,1b
Wang Yangyang1a
1. a. School of Information & Electrical Engineering, b. Hebei Key Laboratory of Security & Protection Information Sensing & Processing, Hebei University of Engineering, Handan Hebei 056038, China
2. School of Internet of Things Engineering, Jiangnan University, Wuxi Jiangsu 214122, China
DOI: 10.19734/j.issn.1001-3695.2021.11.0631

Abstract

With the rapid development of the Internet, the recommendation system can handle the problem of information overload. Due to many problems in traditional recommendation systems, they can't handle the discovery of hidden information. This paper proposed an adaptive graph convolution attention neural collaborative filtering recommendation model(ANGCACF). Firstly, graph convolution neural network obtained the user-item interaction figure and adaptively aggregated the user-item feature information. Secondly, the model added adaptive extended data to solve the data sparsity for the user-item feature information and used the attention mechanism to redistribute the weight to the user-item feature information and the adaptive extended data. Finally, it obtained the final recommendation result by using the algorithm framework of collaborative filtering based on matrix decomposition. Experiments on MovieLens-1M, MovieLens-100K and Amazon-book show that the algorithm is superior to the baseline method in five indexes: precision, recall, Mrr, hit and NDCG.

Foundation Support

国家自然科学基金资助项目(61802107)
教育部—中国移动科研基金资助项目(MCM20170204)
河北省高等学校科学技术研究项目(ZD2020171)
江苏省博士后科研资助计划项目(1601085C)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.11.0631
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 6
Section: Algorithm Research & Explore
Pages: 1760-1766
Serial Number: 1001-3695(2022)06-027-1760-07

Publish History

[2022-01-25] Accepted Paper
[2022-06-05] Printed Article

Cite This Article

杜雨晅, 王巍, 张闯, 等. 基于自适应图卷积注意力神经协同推荐算法 [J]. 计算机应用研究, 2022, 39 (6): 1760-1766. (Du Yuxuan, Wang Wei, Zhang Chuang, et al. Collaborative filtering recommendation algorithm based on adaptive neural graph convolution attention neural network [J]. Application Research of Computers, 2022, 39 (6): 1760-1766. )

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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