%0 Journal Article
%T Context-Aware Bandits
%A Shuai Li
%A Alexandros Karatzoglou
%J Computer Science
%D 2015
%I arXiv
%X In this paper, we present a simple and efficient Context-Aware Bandit (CAB) algorithm. With CAB we attempt to craft a bandit algorithm that can capture collaborative effects and that can be easily deployed in a real-world recommendation system, where the multi-armed bandits have been shown to perform well in particular with respect to the cold-start problem. CAB utilizes a context-aware clustering technique augmenting exploration-exploitation strategies. CAB dynamically clusters the users based on the content universe under consideration. We provide a theoretical analysis in the standard stochastic multi-armed bandits setting. We demonstrate the efficiency of our approach on production and real-world datasets, showing the scalability and, more importantly, the significantly increased prediction performance against several existing state-of-the-art methods.
%U http://arxiv.org/abs/1510.03164v2