Counterfactual Inference for Consumer Choice Across Many Product Categories
跨多产品类别消费者选择的反事实推断AI 解读 ↓
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跨多产品类别消费者选择的反事实推断
- 研究问题
- 消费者在多个类别中并行选择、跨产品偏好与价格敏感性相关时，如何联合估计异质性偏好并分析个性化折扣的反事实？
- 方法与思路
- 作者假定跨类别效用可加，扩展概率矩阵分解以处理时变属性和缺货，并利用价格变化或缺货周的留出数据评价模型。
- 材料陈述的贡献
- 摘要称跨类别信息汇集改善稀少购买者的偏好估计与价格敏感性区分，并在所考察反事实中提高个性化折扣的收益。
- 阅读关注点
- 应核对价格和缺货变化的识别作用、效用可加与偏好相关结构、留出评价方式，以及预测表现能否支持折扣反事实解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核验。摘要未说明价格内生性的处理或改进数值，不能将预测验证等同于反事实识别。
原文摘要
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.