  Modern platforms leverage randomized experiments to make informed decisions
from a given set of items (``treatments''). As a particularly challenging
scenario, these items may (i) arrive in high volume, with thousands of new
items being released per hour, and (ii) have short lifetime, say, due to the
item's transient nature or underlying non-stationarity that impels the platform
to perceive the same item as distinct copies over time. Thus motivated, we
study a Bayesian multiple-play bandit problem that encapsulates the key
features of the multivariate testing (or ``multi-A/B testing'') problem with a
high volume of short-lived arms. In each round, a set of $k$ arms arrive, each
available for $w$ rounds. Without knowing the mean reward for each arm, the
learner selects a multiset of $n$ arms and immediately observes their realized
rewards. We aim to minimize the loss due to not knowing the mean rewards,
averaged over instances generated from a given prior distribution. We show that
when $k = O(n^\rho)$ for some constant $\rho>0$, our proposed policy has
$\tilde O(n^{-\min \{\rho, \frac 12 (1+\frac 1w)^{-1}\}})$ loss on a
sufficiently large class of prior distributions. We complement this result by
showing that every policy suffers $\Omega (n^{-\min \{\rho, \frac 12\}})$ loss
on the same class of distributions. We further validate the effectiveness of
our policy through a large-scale field experiment on {\em Glance}, a
content-card-serving platform that faces exactly the above challenge. A simple
variant of our policy outperforms the platform's current recommender by 4.32\%
in total duration and 7.48\% in total number of click-throughs.
