Affiliations: [a] School of Art and Design, Jiangsu Ocean University, Lianyungang, Jiangsu, China | [b] Faculty of Innovation and Design, City University of Macau, Macao, China | [c] School of Design & Arts, Beijing Institute of Technology, Beijing, China
Correspondence:
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Corresponding author: Yin Cui, School of Design & Arts, Beijing Institute of Technology, Beijing 100081, China. E-mail: [email protected].
Abstract: In an age of big data and information overload, recommendation systems have evolved rapidly. Throughout the traditional design of interior spaces, the specialised nature of the work and the high rate of human involvement has led to high costs. With the continuous development of artificial intelligence technology, it provides a favourable environment for reducing the development cost of the system. This study proposes a two-stage modelling scheme based on deep learning networks for the intelligent design of display space layouts, divided into two parts: matching and layout, which greatly improves design efficiency. The research results show that through comparison tests, its prediction accuracy reaches more than 80%, which can well meet the matching requirements of household products. The training number of Epochs is between 15 and 30, its training curve tends to saturate and the best accuracy can reach 100%, while the running time of the hybrid algorithm proposed in this study is only 20.716 s, which is significantly better compared with other algorithms. The proposed hybrid algorithm has a running time of only 20.716 s, which is significantly better than other algorithms. The approach innovatively combines deep learning technology with computer-aided design (CAD), enabling designers to automatically generate display space layouts with good visibility and usability based on complex design constraints. This study presents an innovative application of the research methodology by combining quantitative and qualitative methods to analyse the data. The application of both methods provides a more comprehensive understanding of the problem under study and provides insight into the key factors that influence the results. The findings of this study can provide useful insights for policy makers and practitioners.
Keywords: Recommendation systems, artificial intelligence, deep learning network, display space layouts, matching requirements
