Online Clustering of Bandits

TL;DR

This paper presents an adaptive clustering algorithm for bandit-based content recommendation, demonstrating improved prediction accuracy and scalability through theoretical analysis and experiments on artificial and real datasets.

Contribution

It introduces a new clustering approach for bandit algorithms, with rigorous regret analysis and empirical validation showing superior performance.

Findings

Significant increase in prediction accuracy over existing methods

Proven scalability of the proposed algorithm

Effective on both artificial and real-world datasets

Abstract

We introduce a novel algorithmic approach to content recommendation based on adaptive clustering of exploration-exploitation ("bandit") strategies. We provide a sharp regret analysis of this algorithm in a standard stochastic noise setting, demonstrate its scalability properties, and prove its effectiveness on a number of artificial and real-world datasets. Our experiments show a significant increase in prediction performance over state-of-the-art methods for bandit problems.