Bridging the Semantic Gap Between Customer Needs and Design Specifications Using User-Generated Content

Although product design has been considered as a collaborative activity, progress in developing methods to facilitate the design process has been hampered by difficulties in building common bases among various stakeholders. Customers may only have general needs of the product in layman's terms instead of the sufficient domain knowledge to identify the product specifications. Thus, there is a great need in design research to translate the expressed needs in natural language to design specifications and bridge this semantic gap. By leveraging the massive amount of online user-generated content, we develop a deep learning-based method to automatically identify product design parameters or product specifications. Specifically, we crawl product review data and the corresponding product metadata from e-commerce websites. A convolutional neural network based solution is provided to map product reviews to product specifications. Experimental results show that the method can be well adapted to the mapping from customer needs in the natural language to product specifications. The method facilitates product design by addressing the semantic gap between general customer needs and detailed product specifications. In addition, the automated mapping greatly improves the efficiency and reduces labor in the design process.