论文标题
收入优化的模型蒸馏：可解释的个性化定价
Model Distillation for Revenue Optimization: Interpretable Personalized Pricing
论文作者
论文摘要
数据驱动的定价策略变得越来越普遍，在该功能中，为客户提供了个性化的价格，这些功能可预测其产品估值。希望这种定价政策简单易用，因此可以对其进行验证，检查公平并轻松实施。但是，将机器学习纳入定价框架中的努力通常会导致不可解释的复杂定价政策，从而导致实践中的采用缓慢。我们提出了一种定制的，基于树木的算法，该算法从复杂的黑盒机器学习算法中提取知识，分割具有相似估值的客户，并以最大化收入的方式开出价格，同时保持可解释性。我们量化了由此产生的政策的遗憾，并证明了其在合成数据集和现实数据集的应用中的功效。
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 which are not interpretable, resulting in slow adoption in practice. We present a 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.
