  This paper considers stochastic bandits with side observations, a model that
accounts for both the exploration/exploitation dilemma and relationships
between arms. In this setting, after pulling an arm i, the decision maker also
observes the rewards for some other actions related to i. We will see that this
model is suited to content recommendation in social networks, where users'
reactions may be endorsed or not by their friends. We provide efficient
algorithms based on upper confidence bounds (UCBs) to leverage this additional
information and derive new bounds improving on standard regret guarantees. We
also evaluate these policies in the context of movie recommendation in social
networks: experiments on real datasets show substantial learning rate speedups
ranging from 2.2x to 14x on dense networks.
