在线学习与显示偏好的利润最大化
Online Learning and Profit Maximization from Revealed Preferences
摘要：
我们考虑的问题，从显示的偏好在网上设置的学习。在我们的框架中，每个时期的消费者购买最佳捆绑的商品从商家根据她的（线性）效用函数和当前的价格，受预算约束。商人只观察购买的商品，并寻求调整价格以优化其利润。我们给出了一个有效的算法，商家的问题，包括一个学习阶段，消费者的效用函数（也许部分）推断，其次是价格优化步骤。我们还考虑了一种替代的在线学习算法，用于设置价格是外生设置的，但商家仍然希望预测消费者出于库存或供应链管理的目的将购买的捆绑包。与大多数先前的工作显示的偏好问题相比，我们表明，通过更强的假设效用函数的形式，有效的算法学习和利润最大化是可能的，即使在自适应，在线设置。
英文摘要：
We consider the problem of learning from revealed preferences in an online setting. In our framework, each period a consumer buys an optimal bundle of goods from a merchant according to her (linear) utility function and current prices, subject to a budget constraint. The merchant observes only the purchased goods, and seeks to adapt prices to optimize his profits. We give an efficient algorithm for the merchant's problem that consists of a learning phase in which the consumer's utility function is (perhaps partially) inferred, followed by a price optimization step. We also consider an alternative online learning algorithm for the setting where prices are set exogenously, but the merchant would still like to predict the bundle that will be bought by the consumer for purposes of inventory or supply chain management. In contrast with most prior work on the revealed preferences problem, we demonstrate that by making stronger assumptions on the form of utility functions, efficient algorithms for both learning and profit maximization are possible, even in adaptive, online settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
