A context-aware diversity-oriented knowledge recommendation approach for smart engineering solution design

Li, Xinyu; Chen, Chun‐Hsien; Zheng, Pai; Jiang, Zuhua; Wang, Linke

doi:10.1016/j.knosys.2021.106739

Cited by 57 publications

(10 citation statements)

References 50 publications

(83 reference statements)

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“…Strengthen the integration of knowledge management and business processes to improve organizational performance. Integrating with management information systems and deploying an incentive mechanism to trigger stakeholders' participation in using/reusing knowledge resources are useful to promote organizational performance [47]. Based on the project mechanism, the workflow is decomposed and organically integrated with the knowledge management process, so that the project process can be tracked.…”

Section: Discussionmentioning

confidence: 99%

Construction and Application of Cloud Computing Model for Reciprocal and燙ollaborative Knowledge Management

Li¹,

Bian²,

Guan³

et al. 2023

Computers, Materials &Amp; Continua

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Promoting the co-constructing and sharing of organizational knowledge and improving organizational performance have always been the core research subject of knowledge management. Existing research focuses on the construction of knowledge management systems and knowledge sharing and transfer mechanisms. With the rapid development and application of cloud computing and big data technology, knowledge management is faced with many problems, such as how to combine with the new generation of information technology, how to achieve integration with organizational business processes, and so on. To solve such problems, this paper proposes a reciprocal collaborative knowledge management model (RCKM model) based on cloud computing technology, reciprocity theory, and collaboration technology. RCKM model includes project group management and cloud computing technology, which can realize management, finance, communication, and quality assurance of multiple projects and solve the problem of business integration with knowledge management. This paper designs evaluation methods of tacit knowledge and reciprocity preference based on the Bayesian formula and analyzes their effect with simulation data. The methods can provide quantitative support for the integration of knowledge management and business management to realize reciprocity and collaboration in the RCKM model. The research found that RCKM model can fully use cloud computing technology to promote the integration of knowledge management and organizational business, and the evaluation method based on the Bayesian formula can provide relatively accurate data support for the evaluation and selection of project team members.

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Section: Discussionmentioning

confidence: 99%

Construction and Application of Cloud Computing Model for Reciprocal and燙ollaborative Knowledge Management

Li¹,

Bian²,

Guan³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…In order to effectively alleviate the problems of data sparsity and cold starts, the data sparsity is reduced by introducing context information [16][17][18], clustering [19][20][21][22] and other methods, and the user's own needs are considered to make the algorithm more efficient. Accuracy is greatly improved.…”

Section: Traditional Recommendation Algorithmsmentioning

confidence: 99%

IT-PMF: A Novel Community E-Commerce Recommendation Method Based on Implicit Trust

Song

Niu

et al. 2022

Mathematics

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It is well-known that data sparsity and cold start are two of the open problems in recommendation system research. Numerous studies have been dedicated to dealing with those two problems. Among these, a method of introducing user context information could effectively solve the problem of data sparsity and improve the accuracy of recommendation algorithms. This study proposed a novel approach called IT-PMF (Implicit Trust-Probabilistic Matrix Factorization) based on implicit trust, which consists of local implicit trust relationships and in-group membership. The study started from generating the user commodity rating matrix based on the cumulative purchases for items according to their historical purchase records to find the similarity of purchase behaviors and the number of successful interactions between users, which represent the local implicit trust relationship between users. The user group attribute value was calculated through a fuzzy c-means clustering algorithm to obtain the user’s in-group membership. The local implicit trust relationship and the user’s in-group membership were adjusted by the adaptive weight to determine the degree of each part’s influence. Then, the author integrated the user’s score of items and the user’s implicit trust relationship into the probabilistic matrix factorization algorithm to form a trusted recommendation model based on implicit trust relationships and in-group membership. The extensive experiments were conducted using a real dataset collected from a community E-commerce platform, and the IT-PMF method had a better performance in both MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) indices compared with well-known existing algorithms, such as PMF (Probabilistic Matrix Factorization) and SVD (Single Value Decomposition). The results of the experiments indicated that the introduction of implicit trust into PMF could improve the quality of recommendations.

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“…The ability of NLP methodologies to process unstructured text opens several opportunities such as topic discovery (Liang et al 2018), ontology extraction (Bouhana et al 2015), document structuring (Morkos, Mathieson, and Summers 2014), search summarisation (Noh, Jo, and Lee 2015), keyword recommendation (Zhang et al 2017) and text generation (Souza, Meireles, and Almeida 2021), which enable design scholars and practitioners to support knowledge reuse (Li et al 2021a), needs elicitation (Lin, Chi, and Hsieh 2012), biomimicry (Shu 2010; Selcuk and Avinc 2021) and emotion-driven design (Dong et al 2021) in the design process. NLP has therefore become an imperative strand of design research, where the scholars have extensively proposed NLP-based tools, frameworks and methodologies that are aimed to assist the participants in the design process, who otherwise often rely upon organisational history and personal knowledge to make important decisions, for example, choosing a lubricant for shaft interface.…”

Section: Introductionmentioning

confidence: 99%

Natural language processing in-and-for design research

2022

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We review the scholarly contributions that utilise natural language processing (NLP) techniques to support the design process. Using a heuristic approach, we gathered 223 articles that are published in 32 journals within the period 1991–present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions and others. Upon summarising and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research.

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A context-aware diversity-oriented knowledge recommendation approach for smart engineering solution design

Cited by 57 publications

References 50 publications

Construction and Application of Cloud Computing Model for Reciprocal and燙ollaborative Knowledge Management

Construction and Application of Cloud Computing Model for Reciprocal and燙ollaborative Knowledge Management

IT-PMF: A Novel Community E-Commerce Recommendation Method Based on Implicit Trust

Natural language processing in-and-for design research

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