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arXiv · 1608.03544

On Context-Dependent Clustering of Bandits

Claudio Gentile·Shuai Li·Purushottam Kar·Alexandros Karatzoglou·Evans Etrue·Giovanni Zappella
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Abstract

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.

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Claudio Gentile, Shuai Li, Purushottam Kar, Alexandros Karatzoglou, Evans Etrue, Giovanni Zappella. 2016-08-06. On Context-Dependent Clustering of Bandits. https://arxiv.org/abs/1608.03544

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