Generating in-store customer journeys from scratch with GPT architectures

Taizo Horikomi () and Takayuki Mizuno ()
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Taizo Horikomi: The Graduate University for Advanced Studies, SOKENDAI
Takayuki Mizuno: The Graduate University for Advanced Studies, SOKENDAI

The European Physical Journal B: Condensed Matter and Complex Systems, 2024, vol. 97, issue 9, 1-9

Abstract: Abstract 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. Graphic abstract

Date: 2024
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DOI: 10.1140/epjb/s10051-024-00778-1

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