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2019 IJCAI IJCAI 2019

Improved Algorithm on Online Clustering of Bandits

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.

Authors

Shuai Li, Wei Chen, Kwong-sak Leung

Topics

Machine Learning > Core Methods > Clustering Machine Learning > Learning Types > Online Learning Machine Learning > Learning Types > Multi-Armed Bandits

Keywords

online clustering multi-armed bandit regret bound user clustering

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