Abstract
We generalize the setting of online clustering of
bandits by allowing non-uniform distribution over
user frequencies. A more efficient algorithm is proposed with simple set structures to represent clusters. We prove a regret bound for the new algorithm which is free of the minimal frequency over
users. The experiments on both synthetic and real
datasets consistently show the advantage of the new
algorithm over existing methods