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Near-Optimal Policies for Dynamic Multinomial Logit Assortment Selection Models

2020-02-14 | |  60 |   40 |   0

Abstract 

In this paper we consider the dynamic assortment selection problem under an uncapacitated multinomial-logit (MNL) model. By carefully analyzing a revenue potential function, we show that a trisection based algorithm achieves an item-independent regret bound of image.png which matches information theoretical lower bounds up to iterated logarithmic terms. Our proof technique draws tools from the unimodal/convex bandit literature as well as adaptive confidence parameters in minimax multi-armed bandit problems. Keywords: dynamic assortment planning, multinomial logit choice model, trisection algorithm, regret analysis.

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