摘要翻译：
本文提出了一种在离散选择中估计消费者偏好的方法，即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选择。消费者的效用在不同类别中是相加的。她对产品属性的偏好以及她对价格的敏感度因产品而异，并且通常与产品相关。我们基于机器学习文献中关于矩阵分解概率模型的技术，将方法扩展到考虑时变的产品属性和产品脱销。我们使用价格变化或缺货的几周的持有数据来评估模型的性能。我们表明，我们的模型改进了传统的建模方法，孤立地考虑每一个类别。改进的一个来源是该模型能够准确地估计偏好的异质性（通过汇集跨类别的信息）；改进的另一个来源是它能够在训练数据中估计很少或从未在给定类别中购买过的消费者的偏好。利用持有的数据，我们表明我们的模型可以准确地区分哪些消费者对给定产品的价格最敏感。我们考虑反事实，如个人目标价格折扣，表明使用一个更丰富的模型，如我们提出的模型，大大增加了折扣中个性化的好处。
---
英文标题：
《Counterfactual Inference for Consumer Choice Across Many Product
  Categories》
---
作者：
Rob Donnelly, Francisco R. Ruiz, David Blei, and Susan Athey
---
最新提交年份：
2019
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
--
一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
--
一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
--

---
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.
---
PDF链接：
https://arxiv.org/pdf/1906.02635