  We propose an efficient Context-Aware clustering of Bandits (CAB) algorithm,
which can capture collaborative effects. CAB can be easily deployed in a
real-world recommendation system, where multi-armed bandits have been shown to
perform well in particular with respect to the cold-start problem. CAB utilizes
a context-aware clustering augmented by exploration-exploitation strategies.
CAB dynamically clusters the users based on the content universe under
consideration. We give a theoretical analysis in the standard stochastic
multi-armed bandits setting. We show the efficiency of our approach on
production and real-world datasets, demonstrate the scalability, and, more
importantly, the significant increased prediction performance against several
state-of-the-art methods.
