  We investigate an efficient context-dependent clustering technique for
recommender systems based on exploration-exploitation strategies through
multi-armed bandits over multiple users. Our algorithm dynamically groups users
based on their observed behavioral similarity during a sequence of logged
activities. In doing so, the algorithm reacts to the currently served user by
shaping clusters around him/her but, at the same time, it explores the
generation of clusters over users which are not currently engaged. We motivate
the effectiveness of this clustering policy, and provide an extensive empirical
analysis on real-world datasets, showing scalability and improved prediction
performance over state-of-the-art methods for sequential clustering of users in
multi-armed bandit scenarios.
