Generating In-store Customer Journeys from Scratch with GPT Architectures
<|reference_start|>Generating In-store Customer Journeys from Scratch with GPT Architectures: 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.<|reference_end|>
@article{horikomi2024generating,
    title={Generating In-store Customer Journeys from Scratch with GPT
  Architectures},
    author={Taizo Horikomi (1), Takayuki Mizuno (2,1) ((1) The Graduate University
  for Advanced Studies, SOKENDAI, (2) National Institute of Informatics)},
    journal={arXiv preprint arXiv:2407.11081},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11081},
    primaryClass={cs.LG cs.AI}
}