2021-07-01-biggs21a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Model Distillation for Revenue Optimization: Interpretable Personalized Pricing	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 for this pricing policy to 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 that are not interpretable, resulting in slow adoption in practice. We present a novel, customized, prescriptive tree-based algorithm that distills knowledge from a complex black-box machine learning algorithm, segments customers with similar valuations and prescribes prices in such a way that maximizes revenue while maintaining interpretability. We quantify the regret of a resulting policy and demonstrate its efficacy in applications with both synthetic and real-world datasets.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	biggs21a	0	Model Distillation for Revenue Optimization: Interpretable Personalized Pricing	946	956	946-956	946	false	Biggs, Max and Sun, Wei and Ettl, Markus	given family Max Biggs given family Wei Sun given family Markus Ettl	2021-07-01		Proceedings of the 38th International Conference on Machine Learning	139	inproceedings	date-parts 2021 7 1	http://proceedings.mlr.press/v139/biggs21a/biggs21a.pdf