In this paper, we discuss practical limitations of the standard choice-based demand models used in the literature to estimate demand from sales transaction data. We present modifications and extensions of the models and discuss data preprocessing and solution techniques which are useful for practitioners dealing with sales transaction data. Among these, we present an algorithm to split sales transaction data observed under partial availability, we extend a popular Expectation Maximization (EM) algorithm for non-homogeneous product sets, and we develop two iterative optimization algorithms which can handle much of the extensions discussed in the paper.

在本文中，我们讨论了文献中用于基于销售交易数据估算需求的基于标准选择的需求模型的实际局限性。我们介绍了模型的修改和扩展，并讨论了对处理销售交易数据的从业者有用的数据预处理和解决方案技术。在这些算法中，我们提出了一种算法，用于分割在部分可用状态下观察到的销售交易数据，我们针对非均质产品集扩展了一种流行的期望最大化（EM）算法，并且我们开发了两种迭代优化算法，可以处理本文中讨论的大部分扩展。纸。