Improved Algorithm on Online Clustering of Bandits

TL;DR

This paper introduces a generalized online clustering algorithm for bandits that handles non-uniform user distributions, offering improved efficiency and regret bounds, validated through experiments on synthetic and real data.

Contribution

It presents a novel algorithm for online clustering of bandits that accommodates non-uniform user frequencies and provides theoretical regret guarantees.

Findings

The new algorithm outperforms existing methods in experiments.

Regret bounds are independent of minimal user frequency.

Algorithm demonstrates consistent advantages on synthetic and real datasets.

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.