Batched Online Contextual Sparse Bandits with Sequential Inclusion of Features
This work addresses fairness in online decision-making for personalized user experiences, but it is incremental as it builds on existing contextual bandit methods with a novel feature inclusion approach.
The paper tackled the problem of fairness in contextual bandits with sparse linear rewards by excluding irrelevant features, introducing the OBSI algorithm that sequentially includes features based on confidence, and showed superior performance in regret, feature relevance, and compute on synthetic data.
Multi-armed Bandits (MABs) are increasingly employed in online platforms and e-commerce to optimize decision making for personalized user experiences. In this work, we focus on the Contextual Bandit problem with linear rewards, under conditions of sparsity and batched data. We address the challenge of fairness by excluding irrelevant features from decision-making processes using a novel algorithm, Online Batched Sequential Inclusion (OBSI), which sequentially includes features as confidence in their impact on the reward increases. Our experiments on synthetic data show the superior performance of OBSI compared to other algorithms in terms of regret, relevance of features used, and compute.