Summary of Consumer Transactions Simulation Through Generative Adversarial Networks, by Sergiy Tkachuk et al.
Consumer Transactions Simulation through Generative Adversarial Networks
by Sergiy Tkachuk, Szymon Łukasik, Anna Wróblewska
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR); Computational Finance (q-fin.CP)
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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 A novel application of Generative Adversarial Networks (GANs) is proposed to generate synthetic retail transaction data, focusing on a system architecture that combines consumer behavior modeling with stock-keeping unit (SKU) availability constraints to address assortment optimization challenges. By integrating SKU data into the GAN architecture and using sophisticated embedding methods, such as hyper-graphs, the system generates simulated consumer purchase behaviors that reflect the dynamic interplay between consumer behavior and SKU availability. The GAN model generates transactions under stock constraints, demonstrating enhanced realism in simulated transactions compared to earlier studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a special type of AI called Generative Adversarial Networks (GANs) to create fake retail transaction data. It’s like making a movie about what might happen in the future. The goal is to make the fake data look real, so it can be used to test and improve ways to predict what people will buy. The system takes into account things like what products are available and how people behave. This could help retail stores better plan for the future. |
Keywords
» Artificial intelligence » Embedding » Gan » Optimization
