Special Topics in Data Analysis and Knowledge Discovery
|
App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 2
401-407

User profile generation method by fusing multi-granularity information

Shao Yibo1
Qin Yuhua1
Cui Yongjun2
Gao Baoyong1
Zhao Biao1
1. College of Information Science & Technology, Qingdao University of Science & Technology, Qingdao Shandong 266061, China
2. Qingdao Hospital, University of Health & Rehabilitation Sciences(Qingdao Municipal Hospital), Qingdao Shandong 266001, China
DOI: 10.19734/j.issn.1001-3695.2023.05.0234

Abstract

Most of the existing user profile methods lack different granularity text information representation, and there is a noise problem in the feature extraction stage, resulting in the inaccurate construction of the profile. To address these issues, this paper proposed a user profile method based on multi-granularity information fusion, called UP-MGIF. Firstly, it integrated the character-level granularity and the word-level granularity representation vectors in the embedding layer to expand feature content. Secondly, based on the improved bi-directional gated recurrent unit network(Bi-GRU), it designed a hybrid feature extraction model called Bi-GRU-DAE-Attention by combining denoising autoencoder(DAE) and attention mechanism to achieve feature denoising and semantic enhancement. Finally, it input the robust feature vectors into the classifier to achieve user profile generation. Experiments show that the user profile generation method achieves higher classification accuracy than other baseline methods on two profile datasets in the medical and Internet domains, and validate the effectiveness of each module through ablation experiments.

Foundation Support

青岛市科技惠民示范项目(23-2-8-smjk-20-nsh)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.05.0234
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 2
Section: Special Topics in Data Analysis and Knowledge Discovery
Pages: 401-407
Serial Number: 1001-3695(2024)02-012-0401-07

Publish History

[2023-08-03] Accepted Paper
[2024-02-05] Printed Article

Cite This Article

邵一博, 秦玉华, 崔永军, 等. 融合多粒度信息的用户画像生成方法 [J]. 计算机应用研究, 2024, 41 (2): 401-407. (Shao Yibo, Qin Yuhua, Cui Yongjun, et al. User profile generation method by fusing multi-granularity information [J]. Application Research of Computers, 2024, 41 (2): 401-407. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)