MNL-Bandit: A Dynamic Learning Approach to Assortment Selection
MNL-Bandit: A Dynamic Learning Approach to Assortment Selection
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MNL-Bandit：品种选择的动态学习方法
DOI：
10.1287/opre.2018.1832
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
A. Zeevi
中科院分区：
文献类型：
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作者：
Shipra Agrawal;Vashist Avadhanula;Vineet Goyal;A. Zeevi
我们考虑一个动态的品种选择问题，在每一轮的零售商提供了一个子集（品种）的N个可替代产品的消费者，谁选择这些产品之一，根据多项logit（MNL）的选择模型。零售商观察这种选择，目标是动态地学习模型参数，同时优化长度为T的销售范围内的累积收入。我们把这个探索-开发公式称为MNL-Bandit问题。针对该问题的现有方法遵循探索-然后-利用方法，该方法估计参数到期望的准确度，然后将这些估计视为正确的参数值，基于这些估计提供最佳分类。这些方法需要一定的先验知识的“可分性”，确定的基本MNL模型的真实参数，这反过来又是至关重要的，在确定勘探期的长度。（可分性是指真正的最优分类与其他次优选择的可分性。）在本文中，我们给出了一个有效的算法，同时探索和利用，没有任何问题参数的先验知识。此外，该算法是自适应的意义上说，它的性能是接近最佳的“良好分离”的情况下，以及一般的参数设置，这种分离不需要举行。
We consider a dynamic assortment selection problem where in every round the retailer offers a subset (assortment) of N substitutable products to a consumer, who selects one of these products according to a multinomial logit (MNL) choice model. The retailer observes this choice, and the objective is to dynamically learn the model parameters while optimizing cumulative revenues over a selling horizon of length T. We refer to this exploration–exploitation formulation as the MNL-Bandit problem. Existing methods for this problem follow an explore-then-exploit approach, which estimates parameters to a desired accuracy and then, treating these estimates as if they are the correct parameter values, offers the optimal assortment based on these estimates. These approaches require certain a priori knowledge of “separability,” determined by the true parameters of the underlying MNL model, and this in turn is critical in determining the length of the exploration period. (Separability refers to the distinguishability of the true optimal assortment from the other suboptimal alternatives.) In this paper, we give an efficient algorithm that simultaneously explores and exploits, without a priori knowledge of any problem parameters. Furthermore, the algorithm is adaptive in the sense that its performance is near optimal in the “well-separated” case as well as the general parameter setting where this separation need not hold.