  Multinomial logit bandit is a sequential subset selection problem which
arises in many applications. In each round, the player selects a
$K$-cardinality subset from $N$ candidate items, and receives a reward which is
governed by a {\it multinomial logit} (MNL) choice model considering both item
utility and substitution property among items. The player's objective is to
dynamically learn the parameters of MNL model and maximize cumulative reward
over a finite horizon $T$. This problem faces the exploration-exploitation
dilemma, and the involved combinatorial nature makes it non-trivial. In recent
years, there have developed some algorithms by exploiting specific
characteristics of the MNL model, but all of them estimate the parameters of
MNL model separately and incur a regret no better than
$\tilde{O}\big(\sqrt{NT}\big)$ which is not preferred for large candidate set
size $N$. In this paper, we consider the {\it linear utility} MNL choice model
whose item utilities are represented as linear functions of $d$-dimension item
features, and propose an algorithm, titled {\bf LUMB}, to exploit the
underlying structure. It is proven that the proposed algorithm achieves
$\tilde{O}\big(dK\sqrt{T}\big)$ regret which is free of candidate set size.
Experiments show the superiority of the proposed algorithm.
