This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a category, but selects from multiple categories in parallel. The consumer's utility is additive in the different categories. Her preferences about product attributes as well as her price sensitivity vary across products and are in general correlated across products. We build on techniques from the machine learning literature on probabilistic models of matrix factorization, extending the methods to account for time-varying product attributes and products going out of stock. We evaluate the performance of the model using held-out data from weeks with price changes or out of stock products. We show that our model improves over traditional modeling approaches that consider each category in isolation. One source of the improvement is the ability of the model to accurately estimate heterogeneity in preferences (by pooling information across categories); another source of improvement is its ability to estimate the preferences of consumers who have rarely or never made a purchase in a given category in the training data. Using held-out data, we show that our model can accurately distinguish which consumers are most price sensitive to a given product. We consider counterfactuals such as personally targeted price discounts, showing that using a richer model such as the one we propose substantially increases the benefits of personalization in discounts.

翻译：本文提出一种评估消费者离散选择偏好的方法，其中消费者在同一品类内最多选择一种产品，但可同时从多个品类中进行选择。消费者的效用呈跨品类可加性，其对产品属性的偏好及价格敏感度在不同产品间存在差异且通常具有相关性。我们基于矩阵分解概率模型的机器学习技术，拓展了相关方法以处理时变产品属性及缺货情形。通过保留价格变动或产品缺货周的数据对所提模型进行评估，结果表明本模型优于传统将各品类独立建模的方法。改进来源之一在于模型能通过跨品类信息整合准确估计偏好异质性，另一来源则是其能有效评估训练数据中某品类内从未或极少购买消费者的偏好。利用保留数据验证，模型能精准识别对特定产品价格敏感度最高的消费群体。我们进一步考虑个性化价格折扣等反事实情形，证明采用本文提出的更丰富模型能显著提升折扣个性化带来的收益。