Integrating evaluation grid method and support vector regression for automobile trade booth design

Abstract

The automobile shows try to convey a clear product or service message to the audience in a short period of time. Therefore, the steps of materials, shape, display and other aspects need to be carefully designed to provide an important display platform for the business. However, most exhibitors depend on their subjective preferences to decide the size and planning of the booth, which fails to attract the attention of customers. In this paper, the evaluation grid method (EGM) and support vector regression (SVR) are combined to design the automobile booth, which provides an innovation process for booth planning and improves the visual appeal of the booth. Firstly, the EGM is used to interview ten highly involved groups, thus obtaining the evaluation grid diagram of the connecting line among the upper emotional needs, the median design items, and the lower specific elements. Secondly, the importance ranking of upper emotional needs is determined by the grey relational analysis. Finally, the SVR is used to establish a mapping model between key emotional needs and lower design elements, thus obtaining the best combination of booth design features preferred by customers. The verification results show that the proposed method can significantly improve the emotional satisfaction of customers and provide clear trade exhibition guidance for exhibitors.