A Note on a Tight Lower Bound for MNL-Bandit Assortment Selection Models

18 Sep 2017Xi ChenYining Wang

In this short note we consider a dynamic assortment planning problem under the capacitated multinomial logit (MNL) bandit model. We prove a tight lower bound on the accumulated regret that matches existing regret upper bounds for all parameters (time horizon $T$, number of items $N$ and maximum assortment capacity $K$) up to logarithmic factors... (read more)

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