# Towards commands recommender system in BIM authoring tool using transformers

arXiv: https://arxiv.org/abs/2406.10237 · HTML: https://mlsanity.com/paper/2406.10237/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 2 Jun 2024.

## Builds on (most cited first, 3 of 3)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)

## Built on by (most cited first, 2 of 2)

- [Text2BIM: Generating Building Models Using a Large Language Model-based Multi-Agent Framework](https://mlsanity.com/paper/2408.08054/) (2024)
- [Predictive Modeling: BIM Command Recommendation Based on Large-scale Usage Logs](https://mlsanity.com/paper/2504.05319/) (2025)

## Abstract (arXiv)

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.