A Knowledge Recommendation Method for Product Form Design Integrating Crowd-Intelligence Context Similarity and Trust Relationships in Cloud Environments
Jian Chen, Zhaoxuan He, Weiwei Wang, Zhihan Li, Qi Jia · Applied Sciences · 2025
During the cloud-based product form design process, traditional collaborative-filtering recommendation methods fail to effectively calculate similarity metrics or generate relevant knowledge recommendations for newly joined designers, due to their lack of historical knowledge scores, resulting in inefficient knowledge acquisition. Since designers show a clear tendency of professional trust in the knowledge adoption process, they are more inclined to accept knowledge resources recommended by people with similar professional backgrounds to theirs or by authorities in their fields. Therefore, this paper proposes a knowledge recommendation method for product form design integrating crowd-intelligence context similarity and trust relationships in cloud environments. The method first constructs an ontology model and a product form design knowledge ontology, containing task context, designer’s context, and computational context to facilitate the acquisition, storage, processing, and invocation of contextual information and knowledge. Second, the neighboring set of target designers is determined by calculating the multidimensional contextual similarity and trust relationship between designers. Finally, the missing knowledge score of the target designer is predicted by the knowledge evaluation of the neighboring designers, and the recommendation list is generated. The method’s effectiveness and feasibility are confirmed through a case study of coffee machine product form design.