Search Machine Learning Repository: Online Clustering of Bandits
Authors: Claudio Gentile, Shuai Li and Giovanni Zappella
Conference: Proceedings of the 31st International Conference on Machine Learning (ICML-14)
Year: 2014
Pages: 757-765
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.
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