  In this work, we study recommendation systems modelled as contextual
multi-armed bandit (MAB) problems. We propose a graph-based recommendation
system that learns and exploits the geometry of the user space to create
meaningful clusters in the user domain. This reduces the dimensionality of the
recommendation problem while preserving the accuracy of MAB. We then study the
effect of graph sparsity and clusters size on the MAB performance and provide
exhaustive simulation results both in synthetic and in real-case datasets.
Simulation results show improvements with respect to state-of-the-art MAB
algorithms.
