https://doi.org/10.1140/epjb/s10051-024-00778-1
Regular Article - Statistical and Nonlinear Physics
Generating in-store customer journeys from scratch with GPT architectures
1
The Graduate University for Advanced Studies, SOKENDAI, Shonan Village, 240-0193, Hayama, Kanagawa, Japan
2
National Institute of Informatics, 2-1-2 Hitotsubashi, 101-0003, Chiyoda, Tokyo, Japan
Received:
31
March
2024
Accepted:
29
August
2024
Published online:
26
September
2024
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.
© The Author(s) 2024
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
