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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 4
1037-1043

Light relational graph attention recommendation model fusing with profiles and text information

Liu Chao
Zhu Bo
College of Computer Science & Engineering, Chongqing University of Technology, Chongqing 400054, China
DOI: 10.19734/j.issn.1001-3695.2022.08.0428

Abstract

To address the problem of poor performance in graph neural network based recommendation systems due to the data sparse problem, this paper proposed a light relational graph attention recommendation model(LightRGAN) that fusing with profiles and text information. First, this model used user profiles and item profiles to initialize the embeddings of users and the items. Secondly, it introduced comments, descriptions and categories as auxiliary information. Meanwhile, it mined the relationships between the text in the comment set and the description set of the same user through the text embedding network based on the multi-head attention mechanism. Then, it learned the embedding representations of the users and the items through a light relational graph convolutional network fused with the attention mechanism. Finally, it weighted sum the embedding representations at each layer and inputted them to the prediction network to calculated match score between the users and the items. The results on three public datasets show that the LightRGAN outperforms other baseline models. The evaluation metrics HR@20 and NDCG@20 improved at least 2.58% and 2.37% than the optimal baseline model.

Foundation Support

国家教育考试科研规划课题(GJK2019006)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.08.0428
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 4
Section: Algorithm Research & Explore
Pages: 1037-1043
Serial Number: 1001-3695(2023)04-013-1037-07

Publish History

[2022-11-08] Accepted Paper
[2023-04-05] Printed Article

Cite This Article

刘超, 朱波. 融合画像和文本信息的轻量级关系图注意推荐模型 [J]. 计算机应用研究, 2023, 40 (4): 1037-1043. (Liu Chao, Zhu Bo. Light relational graph attention recommendation model fusing with profiles and text information [J]. Application Research of Computers, 2023, 40 (4): 1037-1043. )

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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