Smart customization service is an important element for smart manufacturing. The success of smart customization requires that designers, manufacturers, and customers with differences in context, semantics, and other cognitive aspects be engaged in a collaborative process. With product configurators reported to have positive impacts on product quality to meet customers' needs, this article attempts to explore an approach for smart customization service based on configurators. To better address the semantic gap between customers and designers/manufacturers, a new configuration mechanism is proposed that takes into consideration customer needs using natural language as the input and maps them to product specifications in the design stage. We collected a massive amount of review text from e-commerce websites and used ELMo, a contextualized word representation based on a deep bidirectional language model, to encode the text. A multitask learning-based neural network was adopted to build the mapping from layman customer needs to product specifications. Our experiments show that this approach can achieve a promising performance for the configuration task and, thereby, facilitate smart customization services. Note to Practitioners-Smart customization has been adopted by various industries to tailor companies' business streams and customize solutions for customers. It is a complicated service process involving cross-functional teams for identifying customer needs and establishing product design and manufacturing specifications. However, communication in the collaborative process may be challenging. Customers may express their needs using layman's terms. The expressed needs can even be ambiguous and imprecise. Miscommunication hinders the efficiency of smart customization services. This article uses natural language processing and machine learning techniques to map product review text, which is crawled from e-commerce websites to the corresponding product specifications. Given a new customer's needs in free-form text, the mapping can automatically identify the satisfactory product configurations. This has the potential to improve the efficiency of product customization and shield customers and companies from the back-and-forth communication procedure in the customization service.

中文翻译：

---

基于配置的智能定制服务：一种多任务学习方法


智能定制服务是智能制造的重要组成部分。智能定制的成功需要在上下文、语义和其他认知方面存在差异的设计师、制造商和客户参与协作过程。据报道，产品配置器对满足客户需求的产品质量产生积极影响，本文尝试探索一种基于配置器的智能定制服务的方法。为了更好地解决客户和设计者/制造商之间的语义差距，提出了一种新的配置机制，该机制使用自然语言作为输入来考虑客户需求，并在设计阶段将其映射到产品规格。我们从电商网站收集了大量的评论文本，并使用ELMo（一种基于深度双向语言模型的上下文化单词表示）对文本进行编码。采用基于多任务学习的神经网络来构建从外行客户需求到产品规格的映射。我们的实验表明，这种方法可以在配置任务中实现良好的性能，从而促进智能定制服务。从业者须知——智能定制已被各行业采用，为企业量身定制业务流程，为客户定制解决方案。这是一个复杂的服务流程，涉及跨职能团队来确定客户需求并建立产品设计和制造规范。然而，协作过程中的沟通可能具有挑战性。客户可以用通俗易懂的语言表达自己的需求。所表达的需求甚至可能是含糊和不精确的。 沟通不畅阻碍了智能定制服务的效率。本文使用自然语言处理和机器学习技术将从电子商务网站爬取的产品评论文本映射到相应的产品规格。给定新客户的自由格式文本需求，映射可以自动识别满意的产品配置。这有可能提高产品定制的效率，并使客户和公司免受定制服务中来回沟通过程的影响。