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Joint Assortment Optimization and Customization under a Mixture of Multinomial Logit Models: On the Value of Personalized Assortments

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Document pages: 42 pages

Abstract: We consider a joint assortment optimization and customization problem under a mixture of multinomial logit models. In this problem, a firm faces customers of different types, each making a choice within an offered assortment according to the multinomial logit model with different parameters. The problem takes place in two stages. In the first stage, the firm picks an assortment of products to carry subject to a cardinality constraint. In the second stage, a customer of a certain type arrives into the system. Observing the type of the customer, the firm customizes the assortment that it carries by, possibly, dropping products from the assortment. The goal of the firm is to find an assortment to carry and a customized assortment for each customer type that can arrive in the second stage to maximize the expected revenue from a customer visit. The problem arises, for example, in online platforms, where retailers commit to a selection of products before the start of the selling season, but they could potentially customize the displayed assortments for each customer type. We refer to this problem as the Customized Assortment Problem (CAP). Letting m be the number of customer types, we show that the expected revenue of CAP can be $ Omega(m)$ times greater than the optimal expected revenue of the corresponding model without customization and this bound is tight. We establish that CAP is NP-hard to approximate within a factor better than (1-1 e), so we focus on providing an approximation framework for CAP. As our main technical contribution, we design a novel algorithm, which we refer to as Augmented Greedy, and building on it, we give a $ Omega(1 log m)$-approximation algorithm to CAP. Lastly, we present a fully polynomial-time approximation scheme for CAP when the number of customer types is constant. In our computational experiments, we demonstrate the value of customization by using a dataset from Expedia and check the practical performance of our approximation algorithm.

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