A Correlation-Experience-Demand Based Personalized Knowledge Recommendation Approach

Knowledge recommendation is an important means of knowledge reuse that can improve the efficiency and quality of product design. However, at present, there is no good way to fully consider the personalized demands of designers while ensuring the applicability of the recommendation results. Previous...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.61811-61830
Main Authors: Yin, Xiyan, Sheng, Buyun, Zhao, Feiyu, Wang, Xinggang, Xiao, Zheng, Wang, Hui
Format: Article
Language:English
Subjects:
Collaboration
Collaborative filtering
Computational modeling
Correlation
Customization
degree of assistance
degree of correlation
degree of demand
Design
Designers
Knowledge
knowledge recommendation
Machine shops
Machine tools
Mathematical model
Numerical controls
Ontologies
ontology model
Product design
Ranking
Recommender systems
Task analysis
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Summary:Knowledge recommendation is an important means of knowledge reuse that can improve the efficiency and quality of product design. However, at present, there is no good way to fully consider the personalized demands of designers while ensuring the applicability of the recommendation results. Previous studies have usually been based on the similarity between tasks and knowledge or use collaborative filtering technology to accomplish knowledge recommendation. However, these methods do not consider the personal experience of designers and the characteristics of knowledge. This paper proposes a knowledge recommendation approach that integrates the degree of correlation between knowledge and tasks, the feedback-based personal experience, the collective experience of designers, and the degree of demand for knowledge based on the forgetting curve. A knowledge assistance score is generated based on these factors, and the knowledge recommendation list is obtained by ranking the knowledge in descending order of this score. Finally, the approach is applied to a machine shop layout design task and a computer numerical control (CNC) machine tool's spindle design and bearings selection task. The experimental results on two tasks demonstrate that the proposed approach outperforms three baselines on three ranking oriented evaluation metrics. This approach can effectively shorten the time for designers to acquire knowledge by recommending applicable knowledge to assist designers in completing design tasks with high quality and efficiency.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2916350