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Near-Optimal Policies for Dynamic Multinomial Logit Assortment Selection Models
Yining Wang · Xi Chen · Yuan Zhou

Wed Dec 05 02:00 PM -- 04:00 PM (PST) @ Room 517 AB #102

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 O(sqrt(T log log T), 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.

Author Information

Yining Wang (CMU)
Xi Chen (NYU)
Yuan Zhou (Indiana University Bloomington)

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