The price demand relation is a fundamental concept that models how price affects the sale of a product. It is critical to have an accurate estimate of its parameters, as it will impact the company’s revenue. The learning has to be performed very efficiently using a small window of a few test points, because of the rapid changes in price demand parameters due to seasonality and fluctuations. However, there are conflicting goals when seeking the two objectives of revenue maximization and demand learning, known as the learn/earn trade-off. This is akin to the exploration/exploitation trade-off that we encounter in machine learning and optimization algorithms. In this paper, we consider the problem of price demand function estimation, taking into account its exploration–exploitation characteristic. We design a new objective function that combines both aspects. This objective function is essentially the revenue minus a term that measures the error in parameter estimates. Recursive algorithms that optimize this objective function are derived. The proposed method outperforms other existing approaches.

价格需求关系是模拟价格如何影响产品销售的基本概念。准确估计其参数至关重要，因为它将影响公司的收入。由于季节性和波动导致价格需求参数快速变化，因此必须使用几个测试点的小窗口非常有效地执行学习。然而，在寻求收入最大化和需求学习这两个目标时存在相互冲突的目标，这被称为学习/赚取权衡。这类似于我们在机器学习和优化算法中遇到的探索/开发权衡。在本文中，我们考虑了价格需求函数估计问题，同时考虑了其探索-开发特征。我们设计了一个结合了这两个方面的新目标函数。这个目标函数本质上是收入减去衡量参数估计误差的一项。推导出优化该目标函数的递归算法。所提出的方法优于其他现有方法。