Estimating consumer preferences is central to many problems in economics and marketing. This paper develops a flexible framework for learning individual preferences from partial ranking information by interpreting observed rankings as collections of pairwise comparisons with logistic choice probabilities. We model latent utility as the sum of interpretable product attributes, item fixed effects, and a low-rank user-item factor structure, enabling both interpretability and information sharing across consumers and items. We further correct for selection in which comparisons are observed: a comparison is recorded only if both items enter the consumer's consideration set, inducing exposure bias toward frequently encountered items. We model pair observability as the product of item-level observability propensities and estimate these propensities with a logistic model for the marginal probability that an item is observable. Preference parameters are then estimated by maximizing an inverse-probability-weighted (IPW), ridge-regularized log-likelihood that reweights observed comparisons toward a target comparison population. To scale computation, we propose a stochastic gradient descent (SGD) algorithm based on inverse-probability resampling, which draws comparisons in proportion to their IPW weights. In an application to transaction data from an online wine retailer, the method improves out-of-sample recommendation performance relative to a popularity-based benchmark, with particularly strong gains in predicting purchases of previously unconsumed products.

翻译：消费者偏好估计是经济学与市场营销领域众多核心问题的关键。本文提出了一种灵活的框架，通过将观测到的排序信息解释为具有逻辑选择概率的成对比较集合，从部分排序数据中学习个体偏好。我们将潜在效用建模为可解释的产品属性、商品固定效应以及低秩用户-商品因子结构之和，从而同时实现跨消费者与商品的可解释性与信息共享。我们进一步校正了观测比较数据中的选择偏差：仅当两个商品同时进入消费者的考虑集时，比较记录才会被生成，这导致频繁接触的商品存在曝光偏差。我们将成对可观测性建模为商品层面可观测倾向的乘积，并通过商品可观测边际概率的逻辑模型估计这些倾向。随后通过最大化逆概率加权（IPW）的岭正则化对数似然函数来估计偏好参数，该函数将观测比较向目标比较群体进行重新加权。为提升计算效率，我们提出基于逆概率重采样的随机梯度下降（SGD）算法，该算法按IPW权重比例抽取比较样本。在应用于某在线葡萄酒零售商交易数据的实验中，相较于基于流行度的基准方法，本方法显著提升了样本外推荐性能，尤其在预测未消费过产品的购买行为方面表现出显著优势。