In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 7
2025-2032

Multi-view contrast fusion cold start recommendation algorithm based on meta-learning

Zhang Ziyang
Liu Xiaoyang
School of Computer Science & Engineering, Chongqing University of Technology, Chongqing 400054, China
DOI: 10.19734/j.issn.1001-3695.2023.11.0547

Abstract

Addressing the challenges faced by current cold start recommendation models in effectively mining structural and semantic information in heterogeneous information networks, and their tendency to overlook user behavior attributes within these networks, this paper introduced a meta-learning-based multi-view contrast fusion cold start recommendation algorithm(MVC-ML). This algorithm effectively tackled the cold start problem at both the model and data layers. Within the MVC-ML framework, it firstly extracted higher-order semantic information from heterogeneous information networks using a meta-path view. Subsequently, it captured the network's structural features using a network pattern view. Following this, the algorithm analyzed user behavior attribute information through a clustering view. Finally, MVC-ML employed a contrast learning method to integrate the information extracted from these three views, thus generating accurate representation vectors. Experimental validations on datasets, including DBook, demonstrate that the MVC-ML model, in a cold start scenario, reduces MAE by 1.67%, lowers RMSE by 2.06%, and increases nDCG@K by 1.48% compared to traditional heterogeneous information network models such as MetaHIN. These results fully confirm the rationality and effectiveness of the MVC-ML algorithm.

Foundation Support

重庆市社科联重点项目(2023NDZD09)
重庆市教委人文社科重点项目(23SKGH247)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.11.0547
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 7
Section: Algorithm Research & Explore
Pages: 2025-2032
Serial Number: 1001-3695(2024)07-015-2025-08

Publish History

[2024-01-24] Accepted Paper
[2024-07-05] Printed Article

Cite This Article

张子扬, 刘小洋. 基于元学习的多视图对比融合冷启动推荐算法 [J]. 计算机应用研究, 2024, 41 (7): 2025-2032. (Zhang Ziyang, Liu Xiaoyang. Multi-view contrast fusion cold start recommendation algorithm based on meta-learning [J]. Application Research of Computers, 2024, 41 (7): 2025-2032. )

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)