MNL-Bandit: A Dynamic Learning Approach to Assortment Selection

Shipra Agrawal (), Vashist Avadhanula (), Vineet Goyal () and Assaf Zeevi ()
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Shipra Agrawal: Department of Industrial Engineering and Operations Research, Fu Foundation School of Engineering and Applied Science, Columbia University, New York, New York 10027
Vashist Avadhanula: Decision, Risk, and Operations Division, Columbia Business School, Columbia University, New York, New York 10027
Vineet Goyal: Department of Industrial Engineering and Operations Research, Fu Foundation School of Engineering and Applied Science, Columbia University, New York, New York 10027
Assaf Zeevi: Decision, Risk, and Operations Division, Columbia Business School, Columbia University, New York, New York 10027

Operations Research, 2019, vol. 67, issue 5, 1453-1485

Abstract: 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.

Keywords: exploration–exploitation; assortment optimization; upper confidence bound; multinomial logit (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (11)

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