跨多个产品类别的消费者选择反事实推断
Counterfactual Inference for Consumer Choice Across Many Product Categories
AI总结：
本文提出一种跨多产品类别的离散选择模型，利用矩阵分解概率方法处理时变属性和缺货，并通过跨类别信息汇集更准确地估计消费者偏好异质性与价格敏感度，从而提升个性化折扣的反事实收益。
摘要：
本文提出了一种在离散选择中估计消费者偏好的方法，其中消费者在一个类别中最多选择一种产品，但同时从多个类别中进行选择。消费者的效用在不同类别之间是可加的。她对产品属性的偏好以及对价格的敏感度因产品而异，并且通常在不同产品之间存在相关性。我们借鉴了机器学习文献中关于矩阵分解概率模型的技术，并扩展这些方法以考虑随时间变化的产品属性和产品缺货的情况。我们使用价格变动或产品缺货周的留出数据来评估模型的性能。我们表明，我们的模型优于将每个类别孤立考虑的传统建模方法。改进的一个来源是该模型能够（通过跨类别汇集信息）准确估计偏好的异质性；另一个改进来源是它能够估计在训练数据中很少或从未在给定类别中购买过的消费者的偏好。利用留出数据，我们表明我们的模型能够准确区分哪些消费者对给定产品的价格最敏感。我们考虑了诸如个性化定向价格折扣之类的反事实情景，结果表明，使用像我们提出的这样更丰富的模型能够大幅提高折扣个性化带来的收益。
英文摘要：
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.
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。
