  We propose a method that can generate customer trajectories and purchasing
behaviors in retail stores simultaneously using Transformer-based deep learning
structure. Utilizing customer trajectory data, layout diagrams, and retail
scanner data obtained from a retail store, we trained a GPT-2 architecture from
scratch to generate indoor trajectories and purchase actions. Additionally, we
explored the effectiveness of fine-tuning the pre-trained model with data from
another store. Results demonstrate that our method reproduces in-store
trajectories and purchase behaviors more accurately than LSTM and SVM models,
with fine-tuning significantly reducing the required training data.
