Efficient Algorithms for Dynamic Assortment Optimization under Multinomial Logit Choice Model

We develop a unified algorithmic framework that provides provable approximation guarantees for dynamic assortment optimization problems under the Multinomial Logit choice model, improving upon the state-of-the-art results. Our algorithms address both the dynamic assortment problem without personalization and the dynamic assortment problem with personalization, and can handle uncertainty in the total number of customers.