Interactions Between Learning and Broadcasting in Wireless Recommendation Systems

Abstract

We consider recommendation systems that need to operate under wireless bandwidth constraints, which is measured as the number of broadcast transmissions. We demonstrate a (tight for some instances) tradeoff between regret and bandwidth for wireless recommendations formulated in a contextual multiarmed bandit framework.