Context-Aware Bandits

TL;DR

The paper introduces CAB, a context-aware clustering algorithm for bandits that improves recommendation systems by dynamically grouping users and enhancing prediction accuracy, especially in cold-start scenarios.

Contribution

It presents a novel clustering-based bandit algorithm that captures collaborative effects and demonstrates theoretical and empirical advantages over existing methods.

Findings

CAB outperforms state-of-the-art methods on real-world datasets.

Theoretical analysis confirms the efficiency of the approach.

Scalability and improved prediction performance are demonstrated.

Abstract

We propose an efficient Context-Aware clustering of Bandits (CAB) algorithm, which can capture collaborative effects. CAB can be easily deployed in a real-world recommendation system, where multi-armed bandits have been shown to perform well in particular with respect to the cold-start problem. CAB utilizes a context-aware clustering augmented by exploration-exploitation strategies. CAB dynamically clusters the users based on the content universe under consideration. We give a theoretical analysis in the standard stochastic multi-armed bandits setting. We show the efficiency of our approach on production and real-world datasets, demonstrate the scalability, and, more importantly, the significant increased prediction performance against several state-of-the-art methods.