Learning an Optimal Assor
Learning an Optimal Assortment Policy under Observational Data
Learning an Optimal Assortment Policy under Observational Data
arXiv:2502.06777v1 Announce Type: cross
Abstract: We study the fundamental problem of offline assortment optimization under the Multinomial Logit (MNL) model, where sellers must determine the optimal subset of the products to offer based solely on historical customer choice data. While most existin…
