Context-Aware Bandits
This addresses the cold-start problem in recommendation systems, offering an incremental improvement through context-aware clustering.
The paper tackles the cold-start problem in recommendation systems by proposing the Context-Aware Bandits (CAB) algorithm, which dynamically clusters users based on content and shows significant performance gains against state-of-the-art methods on production and real-world datasets.
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.