论文标题
反复暴露和依赖产品的耐心成本的分类优化
Assortment Optimization with Repeated Exposures and Product-dependent Patience Cost
论文作者
论文摘要
在本文中，我们研究了许多在线零售商（例如亚马逊）面临的分类优化问题。我们基于经典的多项式logit模型开发了一个\ emph {Cascade多项式logit模型}，以捕获消费者在多个阶段的购买行为。与现有研究不同，我们的模型允许重复接触产品，即可以在不同阶段多次显示相同的产品。此外，每个消费者都有一个\ emph {耐心预算}，该{耐心预算}是从已知分布中取样的，并且每个产品都与\ emph {耐心成本}相关联，该{耐心成本}捕获了用于浏览该产品的认知工作。鉴于各种产品，消费者逐个阶段依次浏览它们。在一个阶段浏览所有产品之后，如果产品的实用性超过了外部选项的效用，则消费者继续购买产品并离开平台。否则，如果所有产品的耐心成本截至那个时刻不超过她的耐心预算，她将继续查看下一阶段。我们建议解决此问题的近似解决方案。
In this paper, we study the assortment optimization problem faced by many online retailers such as Amazon. We develop a \emph{cascade multinomial logit model}, based on the classic multinomial logit model, to capture the consumers' purchasing behavior across multiple stages. Different from existing studies, our model allows for repeated exposures of a product, i.e., the same product can be displayed multiple times across different stages. In addition, each consumer has a \emph{patience budget} that is sampled from a known distribution and each product is associated with a \emph{patience cost}, which captures the cognitive efforts spent on browsing that product. Given an assortment of products, a consumer sequentially browses them stage by stage. After browsing all products in one stage, if the utility of a product exceeds the utility of the outside option, the consumer proceeds to purchase the product and leave the platform. Otherwise, if the patience cost of all products browsed up to that point is no larger than her patience budget, she continues to view the next stage. We propose an approximation solution to this problem.
