期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 17, 期 9, 页码 6192-6199出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.3043315
关键词
Training; Adaptation models; Mass production; Mass customization; Social networking (online); Transfer learning; Neural networks; Configurator design; deep learning; mass customization; text mining; transfer learning
类别
资金
- Hong Kong Research Grant Council, FDS [UGC/FDS14/E06/18, TII-20-2173]
This article introduces a needs-based configurator mechanism that leverages online product-review text from social media. It utilizes transfer learning to adjust the source model to the target customer needs-specifications mapping, resulting in improved configurator performance.
Online product configurators, the prevailing toolkits used to realize mass customization, embody an advanced manufacturing strategy that provides customized products with the efficiency of mass production. Essentially, a product configuration system elicits customer needs and maps those needs to product attribute specifications. However, existing configurators require that customers have the necessary domain knowledge to configure their products, which hinders the application of these configurators in current customer-centric product design and manufacturing processes. In this article, we propose a needs-based configurator mechanism that leverages online product-review text from social media. We build a source model that maps product reviews to attribute specifications using a hybrid bidirectional long short-term memory network that incorporates relevant product information at word and character levels. Transfer learning is then deployed to adapt the source model to the target customer needs-specifications mapping. Our experimental results show that the transfer-learning operation significantly improves the configurator performance.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据