Online Clustering of Bandits
This addresses the challenge of improving recommendation systems through adaptive clustering, offering a scalable solution with proven effectiveness on real-world datasets.
The paper tackles the problem of content recommendation by introducing an online clustering approach for bandit strategies, achieving a significant increase in prediction performance over state-of-the-art methods.
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.