Online Clustering of Bandits

Claudio Gentile, Shuai Li, Giovanni Zappella
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):757-765, 2014.

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 this Paper

BibTeX
@InProceedings{pmlr-v32-gentile14, title = {Online Clustering of Bandits}, author = {Gentile, Claudio and Li, Shuai and Zappella, Giovanni}, booktitle = {Proceedings of the 31st International Conference on Machine Learning}, pages = {757--765}, year = {2014}, editor = {Xing, Eric P. and Jebara, Tony}, volume = {32}, number = {2}, series = {Proceedings of Machine Learning Research}, address = {Bejing, China}, month = {22--24 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v32/gentile14.pdf}, url = {https://proceedings.mlr.press/v32/gentile14.html}, 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.} }
Endnote
%0 Conference Paper %T Online Clustering of Bandits %A Claudio Gentile %A Shuai Li %A Giovanni Zappella %B Proceedings of the 31st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2014 %E Eric P. Xing %E Tony Jebara %F pmlr-v32-gentile14 %I PMLR %P 757--765 %U https://proceedings.mlr.press/v32/gentile14.html %V 32 %N 2 %X 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.
RIS
TY - CPAPER TI - Online Clustering of Bandits AU - Claudio Gentile AU - Shuai Li AU - Giovanni Zappella BT - Proceedings of the 31st International Conference on Machine Learning DA - 2014/06/18 ED - Eric P. Xing ED - Tony Jebara ID - pmlr-v32-gentile14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 32 IS - 2 SP - 757 EP - 765 L1 - http://proceedings.mlr.press/v32/gentile14.pdf UR - https://proceedings.mlr.press/v32/gentile14.html AB - 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. ER -
APA
Gentile, C., Li, S. & Zappella, G.. (2014). Online Clustering of Bandits. Proceedings of the 31st International Conference on Machine Learning, in Proceedings of Machine Learning Research 32(2):757-765 Available from https://proceedings.mlr.press/v32/gentile14.html.

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