• Corpus ID: 220363615

Model Distillation for Revenue Optimization: Interpretable Personalized Pricing

@inproceedings{Biggs2021ModelDF,
  title={Model Distillation for Revenue Optimization: Interpretable Personalized Pricing},
  author={Max Biggs and Wei Sun and Markus Ettl},
  booktitle={ICML},
  year={2021}
}
Data-driven pricing strategies are becoming increasingly common, where customers are offered a personalized price based on features that are predictive of their valuation of a product. It is desirable to have this pricing policy be simple and interpretable, so it can be verified, checked for fairness, and easily implemented. However, efforts to incorporate machine learning into a pricing framework often lead to complex pricing policies which are not interpretable, resulting in mixed results in… 
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