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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