  We consider recommendation systems that need to operate under wireless
bandwidth constraints, measured as number of broadcast transmissions, and
demonstrate a (tight for some instances) tradeoff between regret and bandwidth
for two scenarios: the case of multi-armed bandit with context, and the case
where there is a latent structure in the message space that we can exploit to
reduce the learning phase.
