摘要翻译：
分类优化是许多行业的一个重要问题，如零售业和在线广告，其目标是从一个可替代产品的宇宙中找到一个子集的产品，使销售者的期望收入最大化。该问题的关键挑战之一是对顾客替代行为进行建模。许多基于参数随机效用最大化(RUM)的选择模型已经在文献中被考虑。然而，在所有这些模型中，购买的概率增加，因为我们包括更多的产品到一个分类。通常情况下，这并不是真的，在许多情况下，更多的选择会损害销售。这通常称为选择过载。本文试图通过推广Blanchet等人提出的基于Markov链的选择模型来解决RUM中的这一局限性。（2016年）。作为一个特例，我们证明了我们的模型归结为一个推广的MNL模型，它的无购买吸引力依赖于分类S并且严格地随分类S的大小而增加。同时，我们证明了该模型下的分类优化是NP难的，在合理的假设下，我们给出了完全多项式时间逼近格式(FPTAS)。
---
英文标题：
《A Generalized Markov Chain Model to Capture Dynamic Preferences and
  Choice Overload》
---
作者：
Kumar Goutam, Vineet Goyal, Agathe Soret
---
最新提交年份：
2020
---
分类信息：

一级分类：Economics        经济学
二级分类：Theoretical Economics        理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
--
一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
--
一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
--

---
英文摘要：
  Assortment optimization is an important problem that arises in many industries such as retailing and online advertising where the goal is to find a subset of products from a universe of substitutable products which maximize seller's expected revenue. One of the key challenges in this problem is to model the customer substitution behavior. Many parametric random utility maximization (RUM) based choice models have been considered in the literature. However, in all these models, probability of purchase increases as we include more products to an assortment. This is not true in general and in many settings more choices hurt sales. This is commonly referred to as the choice overload. In this paper we attempt to address this limitation in RUM through a generalization of the Markov chain based choice model considered in Blanchet et al. (2016). As a special case, we show that our model reduces to a generalization of MNL with no-purchase attractions dependent on the assortment S and strictly increasing with the size of assortment S. While we show that the assortment optimization under this model is NP-hard, we present fully polynomial-time approximation scheme (FPTAS) under reasonable assumptions.
---
PDF链接：
https://arxiv.org/pdf/1911.06716