论文标题
从捆绑销售数据中学习消费者偏好
Learning Consumer Preferences from Bundle Sales Data
论文作者
论文摘要
产品捆绑是在线零售中使用的一种常见销售机制。为了设定有利可图的捆绑价格，卖方需要从交易数据中学习消费者的偏好。当客户购买捆绑包或多种产品时，不能使用诸如离散选择模型之类的经典方法来估计客户的估值。在本文中，我们提出了一种使用捆绑销售数据来了解产品对产品的分配的方法。该方法将其降低为估计问题，其中样品受多面体区域进行审查。使用EM算法和蒙特卡洛模拟，我们的方法可以恢复消费者估值的分布。该框架允许未观察到的无购买和聚类的市场细分。我们提供了有关概率模型的可识别性和EM算法的收敛性的理论结果。该方法的性能也被数值证明。
Product bundling is a common selling mechanism used in online retailing. To set profitable bundle prices, the seller needs to learn consumer preferences from the transaction data. When customers purchase bundles or multiple products, classical methods such as discrete choice models cannot be used to estimate customers' valuations. In this paper, we propose an approach to learn the distribution of consumers' valuations toward the products using bundle sales data. The approach reduces it to an estimation problem where the samples are censored by polyhedral regions. Using the EM algorithm and Monte Carlo simulation, our approach can recover the distribution of consumers' valuations. The framework allows for unobserved no-purchases and clustered market segments. We provide theoretical results on the identifiability of the probability model and the convergence of the EM algorithm. The performance of the approach is also demonstrated numerically.
