The complexity of BIM software presents significant barriers to the
widespread adoption of BIM and model-based design within the Architecture,
Engineering, and Construction (AEC) sector. End-users frequently express
concerns regarding the additional effort required to create a sufficiently
detailed BIM model when compared with conventional 2D drafting. This study
explores the potential of sequential recommendation systems to accelerate the
BIM modeling process. By treating BIM software commands as recommendable items,
we introduce a novel end-to-end approach that predicts the next-best command
based on user historical interactions. Our framework extensively preprocesses
real-world, large-scale BIM log data, utilizes the transformer architectures
from the latest large language models as the backbone network, and ultimately
results in a prototype that provides real-time command suggestions within the
BIM authoring tool Vectorworks. Subsequent experiments validated that our
proposed model outperforms the previous study, demonstrating the immense
potential of the recommendation system in enhancing design efficiency.

该研究探索了序贯推荐系统在加速 BIM 建模过程中的潜力，通过将 BIM 软件命令视为可推荐项目，引入了一种新颖的端到端方法，根据用户的历史交互预测下一个最佳命令，结果是在 BIM 创作工具 Vectorworks 中提供实时的命令建议。后续实验验证了我们提出的模型优于以往的研究，展示了推荐系统在提高设计效率方面的巨大潜力。