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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 5
1337-1342

Rating prediction algorithm based on self-attention mechanism and fusion of local & global features

Yi Lei1,2
Ji Shujuan1
1. Shandong Provincial Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science & Technology, Qingdao Shandong 266590, China
2. Dept. of Personnel, Shandong Jianzhu University, Jinan 250101, China
DOI: 10.19734/j.issn.1001-3695.2021.10.0446

Abstract

In order to fully mine nodes' features and better integrate these features simultaneously in the heterogeneous information network, this paper proposed a AMFL&GRec. Firstly, AMFL&GRec used the LeaderRank algorithm to extract the target node' global sequence, and used a meta-path-based heterogeneous information network embedding model to extract the node' local sequence, and used the skip-gram model to learn the node' global and local features. And then it used the self-attention mechanism to learn the preference of the target nodes' local and global features to obtain the feature representation of the target node in a single meta-path. Secondly, it used the self-attention mechanism to fuse the representation of the same node under different meta-paths to obtain the final feature representation. Finally, it utilized a multi-layer perceptron to achieve the task of rating prediction. This paper conducted a large number of experiments on two real datasets. The experimental results verify that the AMFL&GRec algorithm can not only capture the micro(local) structure of densely connected nodes, but also capture the global structure of the node in the network, and finally obtain nodes' overall(local+global) characteristics. At the same time, the experimental results also prove that the AMFL&GRec's rating prediction performance is better than the baselines. It proves that in the heterogeneous information network utilizing the self-attention mechanism to consider the nodes' preferences for local and global features and meta-paths can improve the accuracy of rating prediction.

Foundation Support

国家自然科学基金资助项目(71772107)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.10.0446
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 5
Section: Algorithm Research & Explore
Pages: 1337-1342
Serial Number: 1001-3695(2022)05-009-1337-06

Publish History

[2021-12-17] Accepted Paper
[2022-05-05] Printed Article

Cite This Article

伊磊, 纪淑娟. 基于自注意力机制的局部与全局特征融合的评分预测算法 [J]. 计算机应用研究, 2022, 39 (5): 1337-1342. (Yi Lei, Ji Shujuan. Rating prediction algorithm based on self-attention mechanism and fusion of local & global features [J]. Application Research of Computers, 2022, 39 (5): 1337-1342. )

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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