  Online learning in large-scale structured bandits is known to be challenging
due to the curse of dimensionality. In this paper, we propose a unified
meta-learning framework for a general class of structured bandit problems where
the parameter space can be factorized to item-level. The novel bandit algorithm
is general to be applied to many popular problems,scalable to the huge
parameter and action spaces, and robust to the specification of the
generalization model. At the core of this framework is a Bayesian hierarchical
model that allows information sharing among items via their features, upon
which we design a meta Thompson sampling algorithm. Three representative
examples are discussed thoroughly. Both theoretical analysis and numerical
results support the usefulness of the proposed method.
