Generating In-store Customer Journeys from Scratch with GPT Architectures
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916325634342912 |
|---|---|
| author | Horikomi, Taizo Mizuno, Takayuki |
| author_facet | Horikomi, Taizo Mizuno, Takayuki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11081 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generating In-store Customer Journeys from Scratch with GPT Architectures Horikomi, Taizo Mizuno, Takayuki Machine Learning Artificial Intelligence 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. |
| title | Generating In-store Customer Journeys from Scratch with GPT Architectures |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2407.11081 |