Generating In-store Customer Journeys from Scratch with GPT Architectures
- URL: http://arxiv.org/abs/2407.11081v1
- Date: Sat, 13 Jul 2024 12:35:52 GMT
- Title: Generating In-store Customer Journeys from Scratch with GPT Architectures
- Authors: Taizo Horikomi, Takayuki Mizuno,
- Abstract summary: We propose a method that can generate customer trajectories and purchasing behaviors in retail stores simultaneously.
We trained a GPT-2 architecture from scratch to generate indoor trajectories and purchase actions.
Results demonstrate that our method reproduces in-store trajectories and purchase behaviors more accurately than LSTM and SVM models.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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