具有特征顺序纳入的批量在线上下文稀疏老虎机
Batched Online Contextual Sparse Bandits with Sequential Inclusion of Features
- Metica
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对稀疏和批量数据下的线性奖励上下文老虎机问题，提出OBSI算法通过随置信度提升顺序纳入特征、排除无关特征来保障公平性，实验显示其在遗憾值、特征相关性和计算量上均优于其他算法。
AI中文摘要：
多臂老虎机（MABs）正越来越多地被用于在线平台和电子商务中，以优化个性化用户体验的决策制定。在本研究中，我们聚焦于稀疏性和批量数据条件下的线性奖励上下文老虎机问题。我们提出了一种名为Online Batched Sequential Inclusion（OBSI，在线批量顺序纳入）的新算法，通过在决策过程中排除无关特征来应对公平性挑战，该算法会随着对特征影响奖励的置信度提升而顺序纳入特征。我们在合成数据上的实验表明，OBSI在遗憾值、所用特征的相关性以及计算量方面均优于其他算法。
英文摘要：
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.
