Summary of Advancing Retail Data Science: Comprehensive Evaluation Of Synthetic Data, by Yu Xia et al.
Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Data
by Yu Xia, Chi-Hua Wang, Joshua Mabry, Guang Cheng
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 The proposed framework for assessing synthetic retail data is a comprehensive approach that focuses on fidelity, utility, and privacy. The evaluation criteria are precise, differentiating between continuous and discrete data attributes. Fidelity is measured through stability and generalizability, while utility is demonstrated through the synthetic data’s effectiveness in critical retail tasks such as demand forecasting and dynamic pricing. The framework also safeguards privacy using Differential Privacy, ensuring that the synthetic data maintains a perfect balance between resembling training and holdout datasets without compromising security. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to evaluate synthetic retail data that makes sure it is accurate, useful, and private. Synthetic data is used in many ways in retail, like predicting demand and setting prices dynamically. The framework checks how well the synthetic data does these tasks and ensures it doesn’t compromise customer information. |
Keywords
» Artificial intelligence » Synthetic data
