Triplet Embeddings for Demand Estimation
提出一种利用众包三元组数据（产品A比C更接近B）计算产品空间低维嵌入的方法，可替代混合logit模型中的特征或约束对数线性模型的交叉弹性，以谷物需求估计为例验证了更合理的替代模式和更好的拟合度。
We propose a method to augment conventional demand estimation approaches with crowd-sourced data on the product space. Our method obtains triplets data (“product A is closer to B than it is to C”) from an online survey to compute an embedding—i.e., a low-dimensional representation of the latent product space. The embedding can either replace data on observed characteristics in mixed logit models, or provide pairwise product distances to discipline cross-elasticities in log-linear models. We illustrate both approaches by estimating demand for ready-to-eat cereals; the information contained in the embedding leads to more plausible substitution patterns and better fit.