我们研究了一家公司所面临的定价问题，销售大量的产品，通过广泛的功能，随着时间的推移到达的客户。顾客根据一个包含产品特征和顾客特征的一般选择模型独立地做出购买决策，该模型被编码为$d$维数值向量，以及所提供的价格。选择模型的参数是先验未知的公司，但可以学习（二进制值）的销售数据随着时间的推移。公司的目标是尽量减少遗憾，即，针对预先知道选择模型的参数并且总是提供收入最大化价格的千里眼策略的预期收入损失。这种设置的部分原因是在线市场的流行，允许实时定价。我们假设一个结构化的选择模型，其参数取决于$d$产品特征中的$s_0$。我们提出了一个动态的政策，称为正则化极大似然定价（RMLP），利用（稀疏）结构的高维模型，并获得了对数遗憾$T$。更具体地说，我们的算法的遗憾是$O(s_0 \log d \cdot \log T)$。此外，我们表明，没有政策可以获得比$O(s_0 (\log d + \log T))$更好的遗憾。
英文摘要：
We study the pricing problem faced by a firm that sells a large number of products, described via a wide range of features, to customers that arrive over time. Customers independently make purchasing decisions according to a general choice model that includes products features and customers' characteristics, encoded as $d$-dimensional numerical vectors, as well as the price offered. The parameters of the choice model are a priori unknown to the firm, but can be learned as the (binary-valued) sales data accrues over time. The firm's objective is to minimize the regret, i.e., the expected revenue loss against a clairvoyant policy that knows the parameters of the choice model in advance, and always offers the revenue-maximizing price. This setting is motivated in part by the prevalence of online marketplaces that allow for real-time pricing. We assume a structured choice model, parameters of which depend on $s_0$ out of the $d$ product features. We propose a dynamic policy, called Regularized Maximum Likelihood Pricing (RMLP) that leverages the (sparsity) structure of the high-dimensional model and obtains a logarithmic regret in $T$. More specifically, the regret of our algorithm is of $O(s_0 \log d \cdot \log T)$. Furthermore, we show that no policy can obtain regret better than $O(s_0 (\log d + \log T))$.
Utility Fairness in Contextual Dynamic Pricing with Demand Learning
Leonard N. Stern School of Business, New York University（纽约大学莱昂纳德·N·斯特恩商学院）；Naveen Jindal School of Management, University of Texas at Dallas（德克萨斯大学达拉斯分校纳文·金达尔管理学院）
Dynamic Pricing for Airline Ancillaries with Customer Context
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Imperial College London（帝国理工学院）；Otwell Research（Otwell研究机构）；Deepair solutions（Deepair解决方案公司(注：原文本中机构名首字母小写，按原文提取)）
Dynamic Pricing with Adversarially-Censored Demands
Carnegie Mellon University（卡内基梅隆大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；New York University（纽约大学）；University of California San Diego（加州大学圣地亚哥分校）
Mitchell E. Daniels, Jr. School of Business, Purdue University（普渡大学米切尔·E·丹尼尔斯 Jr.商学院）；Department of Statistics and Data Science, Yale University（耶鲁大学统计与数据科学系）；Department of Industrial Engineering and Management Sciences, Northwestern University（西北大学工业工程与管理科学系）
