  We consider the following variant of contextual linear bandits motivated by
routing applications in navigational engines and recommendation systems. We
wish to learn a hidden $d$-dimensional value $w^*$. Every round, we are
presented with a subset $\mathcal{X}_t \subseteq \mathbb{R}^d$ of possible
actions. If we choose (i.e. recommend to the user) action $x_t$, we obtain
utility $\langle x_t, w^* \rangle$ but only learn the identity of the best
action $\arg\max_{x \in \mathcal{X}_t} \langle x, w^* \rangle$. We design
algorithms for this problem which achieve regret $O(d\log T)$ and $\exp(O(d
\log d))$. To accomplish this, we design novel cutting-plane algorithms with
low "regret" -- the total distance between the true point $w^*$ and the
hyperplanes the separation oracle returns. We also consider the variant where
we are allowed to provide a list of several recommendations. In this variant,
we give an algorithm with $O(d^2 \log d)$ regret and list size
$\mathrm{poly}(d)$. Finally, we construct nearly tight algorithms for a weaker
variant of this problem where the learner only learns the identity of an action
that is better than the recommendation. Our results rely on new algorithmic
techniques in convex geometry (including a variant of Steiner's formula for the
centroid of a convex set) which may be of independent interest.

Machine Learning;Data Structures and Algorithms;Optimization and Control