We consider a sequential assortment selection problem where the user choice is given by multinomial logit (MNL) model whose parameters are unknown. In each period, learning agent observes d-dimensional contextual information about and N available items, offers an of size K to user, bandit feedback item chosen from assortment. propose upper confidence bound based algorithms for this MNL bandit. ...

Unique parametrizations of models are very important for parameter interpretation and consistency of estimators. In this paper we analyze the identifiability of a general class of finite mixtures of multinomial logits with varying and fixed effects, which includes the popular multinomial logit and conditional logit models. The application of the general identifiability conditions is demonstrate...