We investigate a novel cluster-of-bandit algorithm CAB for collaborative recommendation tasks that implements the underlying feedback sharing mechanism by estimating the neighborhood of users in a context-dependent manner. CAB makes sharp departures from the state of the art by incorporating collaborative effects into inference as well as learning processes in a manner that seamlessly interleaving explore-exploit tradeoffs and collaborative steps. We prove regret bounds under various assumptions on the data, which exhibit a crisp dependence on the expected number of clusters over the users, a natural measure of the statistical difficulty of the learning task. Experiments on production and real-world datasets show that CAB offers significantly increased prediction performance against a representative pool of state-of-the-art methods.
@article{arxiv.1608.03544,
title = {On Context-Dependent Clustering of Bandits},
author = {Claudio Gentile and Shuai Li and Purushottam Kar and Alexandros Karatzoglou and Evans Etrue and Giovanni Zappella},
journal= {arXiv preprint arXiv:1608.03544},
year = {2017}
}