用图文嵌入提升需求预测，尤其适合无属性数据场景。
Demand Estimation with Text and Image Data
- 结合图像与文本嵌入，融入混合逻辑需求模型
- 在亚马逊40个品类中均发现图文信息有效揭示替代关系
- 对难以量化的设计等属性有更好预测能力
我们提出一种利用非结构化数据推断替代模式的需求估计方法。通过预训练深度学习模型，从产品图像和文本描述中提取嵌入，并将其引入混合逻辑需求模型。该方法可在缺乏产品属性数据或消费者重视难以量化属性（如视觉设计）时，依然实现有效需求估计。通过选择实验验证，该方法在预测第二选择的反事实情境下，显著优于传统基于属性的模型。我们还将该方法应用于亚马逊网站上的40个产品品类，一致发现非结构化数据对替代模式具有重要信息价值。
原文摘要 · Abstract (English)
We propose a demand estimation approach that leverages unstructured data to infer substitution patterns. Using pre-trained deep learning models, we extract embeddings from product images and textual descriptions and incorporate them into a mixed logit demand model. This approach enables demand estimation even when researchers lack data on product attributes or when consumers value hard-to-quantify attributes such as visual design. Using a choice experiment, we show this approach substantially outperforms standard attribute-based models at counterfactual predictions of second choices. We also apply it to 40 product categories offered on Amazon.com and consistently find that unstructured data are informative about substitution patterns.
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