Online Clustering of Bandits
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.
Cite
Text
Gentile et al. "Online Clustering of Bandits." International Conference on Machine Learning, 2014.Markdown
[Gentile et al. "Online Clustering of Bandits." International Conference on Machine Learning, 2014.](https://mlanthology.org/icml/2014/gentile2014icml-online/)BibTeX
@inproceedings{gentile2014icml-online,
title = {{Online Clustering of Bandits}},
author = {Gentile, Claudio and Li, Shuai and Zappella, Giovanni},
booktitle = {International Conference on Machine Learning},
year = {2014},
pages = {757-765},
volume = {32},
url = {https://mlanthology.org/icml/2014/gentile2014icml-online/}
}