Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 5
1346-1351

Graph contrastive learning recommendation algorithm based on mixed sampling

Yuan Congqia
Liu Yuana,b
Liu Jingwena
a. School of Artificial Intelligence & Computer Science, b. Jiangsu Key Laboratory of Media Design & Software Technology, Jiangnan University, Wuxi Jiangsu 214122, China
DOI: 10.19734/j.issn.1001-3695.2022.10.0533

Abstract

In recommender systems, graph convolutional networks have stronger information extraction capabilities for graph-structured data. However, existing graph convolutional networks mainly focus on enhancing the model structure, ignoring the importance of improving the sampling quality of the original samples and mining the implicit relationship between users and items. Aiming at the above problems, this paper proposed a graph contrastive learning recommendation algorithm based on mixed sampling. Firstly, the algorithm used a mixed sampling method to extract part of the information in positive samples and injected them into negative samples, thereby generating new informative hard negative samples. Secondly, to extract features on hard negative samples, it used the light graph convolution network to obtain node representations of users and items. Finally, it carried out a multi-task strategy to jointly optimize the recommendation supervision task and the contrastive learning task. The experiments on real datasets Yelp2018 and Amazon-book demonstrate that the proposed algorithm improves performance compared with other recommendation algorithms in recall and NDCG evaluation indexes.

Foundation Support

国家自然科学基金资助项目(61972182)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.10.0533
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 5
Section: Algorithm Research & Explore
Pages: 1346-1351
Serial Number: 1001-3695(2023)05-010-1346-06

Publish History

[2023-01-04] Accepted Paper
[2023-05-05] Printed Article

Cite This Article

袁琮淇, 刘渊, 刘静文. 基于混合采样的图对比学习推荐算法 [J]. 计算机应用研究, 2023, 40 (5): 1346-1351. (Yuan Congqi, Liu Yuan, Liu Jingwen. Graph contrastive learning recommendation algorithm based on mixed sampling [J]. Application Research of Computers, 2023, 40 (5): 1346-1351. )

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  • 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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