Summary of Generating In-store Customer Journeys From Scratch with Gpt Architectures, by Taizo Horikomi (1) et al.
Generating In-store Customer Journeys from Scratch with GPT Architectures
by Taizo Horikomi, Takayuki Mizuno
First submitted to arxiv on: 13 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel deep learning approach to simultaneously generate customer trajectories and purchasing behaviors in retail stores using Transformer-based architecture. The model is trained from scratch on a combination of customer trajectory data, layout diagrams, and retail scanner data obtained from a single store, leveraging GPT-2 architecture. Additionally, the authors explore the effectiveness of fine-tuning the pre-trained model with data from another store. The results show that the proposed method outperforms LSTM and SVM models in reproducing in-store trajectories and purchase behaviors, with fine-tuning significantly reducing the required training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to track customer movements and buying habits in stores using special computer models. It uses a combination of map layouts, scanner data, and customer movement data from one store to train a super-smart model that can predict where customers will go and what they’ll buy next. The researchers also tested whether the model could be improved by adding data from another store. They found that this new approach is better than previous methods at predicting customer movements and buying habits. |
Keywords
* Artificial intelligence * Deep learning * Fine tuning * Gpt * Lstm * Transformer
