Contextual Bandit with Adaptive Feature Extraction
This work addresses the problem of enhancing decision-making in online systems for applications like recommendation engines, though it appears incremental as it builds on existing contextual bandit methods with adaptive representation learning.
The paper tackles the contextual bandit problem by introducing an adaptive feature extraction method using online clustering, which improves performance over standard approaches that use raw input contexts, as demonstrated in experiments across various datasets and environments.
We consider an online decision making setting known as contextual bandit problem, and propose an approach for improving contextual bandit performance by using an adaptive feature extraction (representation learning) based on online clustering. Our approach starts with an off-line pre-training on unlabeled history of contexts (which can be exploited by our approach, but not by the standard contextual bandit), followed by an online selection and adaptation of encoders. Specifically, given an input sample (context), the proposed approach selects the most appropriate encoding function to extract a feature vector which becomes an input for a contextual bandit, and updates both the bandit and the encoding function based on the context and on the feedback (reward). Our experiments on a variety of datasets, and both in stationary and non-stationary environments of several kinds demonstrate clear advantages of the proposed adaptive representation learning over the standard contextual bandit based on "raw" input contexts.