Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 10
3044-3048

Collaborative filtering recommendation model based on graph convolution and cross product

Su Jing
Xu Tianqi
Zhang Xiankun
Shi Yancui
Gu Shuting
School of Artificial Intelligence, Tianjin University of Science & Technology, Tianjin 300457, China
DOI: 10.19734/j.issn.1001-3695.2021.02.0053

Abstract

The function of recommendation system is to help users actively finding personalized items that meet theirs preferences and recommend them to users. Collaborative filtering algorithm is a classic algorithm in recommender system, but it is limited by cold start of data and sparsity and has disadvantages such as poor interpretability and poor model generalization ability. This paper studied its shortcomings. By taking the original score matrix in the form of user-project bipartite graph as input, designing the figure convolution neural network as a variant of graph autoencoder, which was obtained by the latent vector of user and item by iteratively aggregating neighbor node information, and combining CNN, this paper proposed a recommendation algorithm based on convolution matrix decomposition to improve the interpretability of the model and generalization ability. And also solved the auxiliary information fusion the data sparseness, and made recommendation performance improved by 1.4% and 1.7%. It provides a new idea for recommendation direction based on graph neural network in the future.

Foundation Support

天津市自然科学基金资助项目(19JCYBJC15300)
天津市教委项目(2018KJ105)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.02.0053
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 10
Section: Algorithm Research & Explore
Pages: 3044-3048
Serial Number: 1001-3695(2021)10-028-3044-05

Publish History

[2021-10-05] Printed Article

Cite This Article

苏静, 许天琪, 张贤坤, 等. 基于图卷积与外积的协同过滤推荐模型 [J]. 计算机应用研究, 2021, 38 (10): 3044-3048. (Su Jing, Xu Tianqi, Zhang Xiankun, et al. Collaborative filtering recommendation model based on graph convolution and cross product [J]. Application Research of Computers, 2021, 38 (10): 3044-3048. )

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  • Application Research of Computers Monthly Journal
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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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