基于深度特征学习和用户偏好感知的制造云服务推荐模型

IF 3.7 3区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS International Journal of Computer Integrated Manufacturing Pub Date : 2024-02-11 DOI:10.1080/0951192x.2024.2314781
Shenquan Huang, Yujie Wang, An Long, Xiaohui Zhu, Luchuan Yu
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引用次数: 0

摘要

制造云服务推荐模型是一项关键技术,可帮助用户从日益扩大的制造云服务中快速发现并获得所需的个性化服务。
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Manufacturing cloud service recommendation model based on deep feature learning and user preference perception
The manufacturing cloud service recommendation model is a key technology for users to quickly discover and obtain the personalized services they need from the increasingly expanding manufacturing c...
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来源期刊
CiteScore
9.00
自引率
9.80%
发文量
73
审稿时长
10 months
期刊介绍: International Journal of Computer Integrated Manufacturing (IJCIM) reports new research in theory and applications of computer integrated manufacturing. The scope spans mechanical and manufacturing engineering, software and computer engineering as well as automation and control engineering with a particular focus on today’s data driven manufacturing. Terms such as industry 4.0, intelligent manufacturing, digital manufacturing and cyber-physical manufacturing systems are now used to identify the area of knowledge that IJCIM has supported and shaped in its history of more than 30 years. IJCIM continues to grow and has become a key forum for academics and industrial researchers to exchange information and ideas. In response to this interest, IJCIM is now published monthly, enabling the editors to target topical special issues; topics as diverse as digital twins, transdisciplinary engineering, cloud manufacturing, deep learning for manufacturing, service-oriented architectures, dematerialized manufacturing systems, wireless manufacturing and digital enterprise technologies to name a few.
期刊最新文献
Integration of extended reality and CAE in the context of industry 4.0 Real-time tool condition monitoring with the internet of things and machine learning algorithms Flexible automation and intelligent manufacturing highlights: special issue editorial State of the art and future directions of digital twin-enabled smart assembly automation in discrete manufacturing industries Tool wear prediction method based on dual-attention mechanism network
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