  In this paper, we investigate the impact of diverse user preference on
learning under the stochastic multi-armed bandit (MAB) framework. We aim to
show that when the user preferences are sufficiently diverse and each arm can
be optimal for certain users, the O(log T) regret incurred by exploring the
sub-optimal arms under the standard stochastic MAB setting can be reduced to a
constant. Our intuition is that to achieve sub-linear regret, the number of
times an optimal arm being pulled should scale linearly in time; when all arms
are optimal for certain users and pulled frequently, the estimated arm
statistics can quickly converge to their true values, thus reducing the need of
exploration dramatically. We cast the problem into a stochastic linear bandits
model, where both the users preferences and the state of arms are modeled as
{independent and identical distributed (i.i.d)} d-dimensional random vectors.
After receiving the user preference vector at the beginning of each time slot,
the learner pulls an arm and receives a reward as the linear product of the
preference vector and the arm state vector. We also assume that the state of
the pulled arm is revealed to the learner once its pulled. We propose a
Weighted Upper Confidence Bound (W-UCB) algorithm and show that it can achieve
a constant regret when the user preferences are sufficiently diverse. The
performance of W-UCB under general setups is also completely characterized and
validated with synthetic data.
