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Improved Algorithm on Online Clustering of Bandits
Author(s)
Date Issued
2019
Citation
IJCAI International Joint Conference on Artificial Intelligence 2019, pp. 2923-2929.
Type
Conference Paper
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.
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