Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 6
1753-1759

Multi-behavior recommendation model based on light graph convolution and enhanced attention

Gao Yulan
Huang Xianying
Tao Jia
School of Computer Science & Engineering, Chongqing University of Technology, Chongqing 400054, China
DOI: 10.19734/j.issn.1001-3695.2021.11.0638

Abstract

In recent years, many researchers take advantage of graph convolution network in multi-behavior recommendation to further alleviate the data sparsity problem. However, most of current works directly use graph convolution network, which makes time complexity of the model relatively high. These works also ignore the different weights of neighbors and the different contributions of each behavior to user's preference. Therefore, this paper proposed a multi-behavior recommendation model based on light graph convolution and enhanced attention(MB-LGCA). Firstly, the model constructed a user-item bipartite graph according to the multi-behavior data, and used a light graph convolution network to aggregate the features of neighbors to obtain high-order collaborative information. At the same time, it used attention mechanism to integrate the neighbors' weights to enhance embedding representations of nodes. It used the k-order user's embedding propagation to obtain the different importance of each behavior to user's preference, so that the model had better interpretability. Finally, it combined embedding representations of different layers for prediction. The experimental results on two real datasets show that the model has better performance.

Foundation Support

重庆市社会科学规划项目(2021NDYB101)
国家自然科学基金资助项目(62141201)
巴南区科技局科技项目(2020QC40)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.11.0638
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 6
Section: Algorithm Research & Explore
Pages: 1753-1759
Serial Number: 1001-3695(2022)06-026-1753-07

Publish History

[2022-02-09] Accepted Paper
[2022-06-05] Printed Article

Cite This Article

高钰澜, 黄贤英, 陶佳. 基于轻量图卷积和注意力增强的多行为推荐模型 [J]. 计算机应用研究, 2022, 39 (6): 1753-1759. (Gao Yulan, Huang Xianying, Tao Jia. Multi-behavior recommendation model based on light graph convolution and enhanced attention [J]. Application Research of Computers, 2022, 39 (6): 1753-1759. )

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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