  Recommender systems should adapt to user interests as the latter evolve. A
prevalent cause for the evolution of user interests is the influence of their
social circle. In general, when the interests are not known, online algorithms
that explore the recommendation space while also exploiting observed
preferences are preferable. We present online recommendation algorithms rooted
in the linear multi-armed bandit literature. Our bandit algorithms are tailored
precisely to recommendation scenarios where user interests evolve under social
influence. In particular, we show that our adaptations of the classic LinREL
and Thompson Sampling algorithms maintain the same asymptotic regret bounds as
in the non-social case. We validate our approach experimentally using both
synthetic and real datasets.
