Floor plan recommendation system using graph neural network with spatial relationship dataset

The purpose of this study was to develop a recommendation system that, in the pre-design phase, quickly and easily search adequate floor plans satisfying the client requirements about the spatial relationship type using artificial intelligence (AI) technology. In this study using a graph dataset representing the spatial relationship between entities, we propose a deep neural network approach using SimGNN and shallow networks with teacher–student learning to compute graph similarity, measured by graph edit distance, fast and accurately during the search operation in the recommendation system. The prediction errors between the GED score (ground truth) and the predicted score were small enough to employ the neural networks for the recommendation system instead of using GED, which takes a long calculation time. The proposed recommendation systems based deep networks also suggested floor plans satisfying given conditions on the spatial relationship with high accuracy.