DC FieldValueLanguage
dc.contributorDepartment of Industrial and Systems Engineeringen_US
dc.creatorLi, Xen_US
dc.creatorChen, CHen_US
dc.creatorZheng, Pen_US
dc.creatorJiang, Zen_US
dc.creatorWang, Len_US
dc.date.accessioned2021-11-09T07:39:20Z-
dc.date.available2021-11-09T07:39:20Z-
dc.identifier.issn0950-7051en_US
dc.identifier.urihttp://hdl.handle.net/10397/91593-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectKnowledge recommendationen_US
dc.subjectDiversityen_US
dc.subjectContext-awareen_US
dc.subjectEngineering solution designen_US
dc.subjectEngineering knowledgeen_US
dc.titleA context-aware diversity-oriented knowledge recommendation approach for smart engineering solution designen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume215en_US
dc.identifier.doi10.1016/j.knosys.2021.106739en_US
dcterms.abstractTo 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.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationKnowledge-based systems, 5 Mar. 2021, v. 215, 106739en_US
dcterms.isPartOfKnowledge-based systemsen_US
dcterms.issued2021-03-05-
dc.identifier.isiWOS:000620459300001-
dc.identifier.artn106739en_US
dc.description.validate202111 bchyen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera1047-n10-
dc.identifier.SubFormID43851-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis work is conducted within the Delta-NTU Corporate Lab for Cyber-Physical Systems with funding support from Delta Electronics Inc and the National Research Foundation (NRF) Singapore under the Corporate Laboratory @ University Scheme (Ref. RCA-16/434; SCO-RP1) at Nanyang Technological University, Singapore. The authors also acknowledge the funding support from the National Natural Science Foundation of China (No. 71671113).en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2023.03.05en_US
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