Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/160698
Title: A context-aware diversity-oriented knowledge recommendation approach for smart engineering solution design
Authors: Li, Xinyu
Chen, Chun-Hsien
Zheng, Pai
Jiang, Zuhua
Wang, Linke
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Li, X., Chen, C., Zheng, P., Jiang, Z. & Wang, L. (2021). A context-aware diversity-oriented knowledge recommendation approach for smart engineering solution design. Knowledge-Based Systems, 215, 106739-. https://dx.doi.org/10.1016/j.knosys.2021.106739
Project: RCA-16/434
SCO-RP1 
Journal: Knowledge-Based Systems
Abstract: To proactively fulfill multiple stakeholders’ needs in the engineering solution design process, the knowledge recommendation approach is adopted as a key element in the knowledge management system. Nevertheless, most existing knowledge recommendation approaches cannot simultaneously meet the higher standard of in-context accuracy and diversity. To address the issue, this paper proposes a context-aware diversity-oriented knowledge recommendation approach, thereby assisting stakeholders to accomplish engineering solution design in a smarter manner. Three diversity concerns, namely item-diversity, context-diversity, and user-diversity are addressed by semantic-based content analysis, context definition and awareness, and user profile modeling, respectively. Hence, the proposed approach not only maximizes the diversity of the recommended knowledge but also guarantees its accuracy under multiple problem-solving contexts. Moreover, a practical engineering solution design case on a Smart 3D printer platform is conducted, to validate the efficacy of the proposed approach in providing usable and diverse knowledge items. It is anticipated this work can provide useful insights to practitioners in their knowledge-based engineering solution design process.
URI: https://hdl.handle.net/10356/160698
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2021.106739
Schools: School of Electrical and Electronic Engineering 
School of Mechanical and Aerospace Engineering 
Rights: © 2021 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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