在数据有限时，利用离线数据高效聚类用户以提升推荐决策
Offline Clustering of Linear Bandits: The Power of Clusters under Limited Data
- 基于离线数据设计聚类算法，解决小样本下用户分组难题
- 算法在数据稀缺时表现优于现有方法，在数据充足时接近理论最优
- 适合缺乏实时反馈的场景，如医疗、金融等高成本决策领域
上下文多臂赌博机是序列决策的基础框架，例如为陆续到来的用户生成广告推荐。近期研究发现，根据用户偏好相似性进行聚类可加速学习。然而，以往工作主要关注在线设置，需持续收集用户数据，忽视了实际应用中广泛存在的离线数据。为此，本文提出离线聚类多臂赌博机（Off-ClusBand）问题，研究如何利用离线数据学习聚类特性并改进决策。核心挑战在于用户数据不足：与在线场景可不断积累数据不同，离线场景仅有固定且有限的数据集，必须判断是否具备足够数据来可靠聚类。为此，本文提出两种算法：Off-C2LUB 在分析和实验上均优于现有方法，尤其在小样本下；Off-CLUB 在数据稀疏时可能引入偏差，但在数据充足时表现良好，近乎达到理论下界。实验在真实和合成数据集上验证了结果。
原文摘要 · Abstract (English)
Contextual multi-armed bandit is a fundamental learning framework for making a sequence of decisions, e.g., advertising recommendations for a sequence of arriving users. Recent works have shown that clustering these users based on the similarity of their learned preferences can accelerate the learning. However, prior work has primarily focused on the online setting, which requires continually collecting user data, ignoring the offline data widely available in many applications. To tackle these limitations, we study the offline clustering of bandits (Off-ClusBand) problem, which studies how to use the offline dataset to learn cluster properties and improve decision-making. The key challenge in Off-ClusBand arises from data insufficiency for users: unlike the online case where we continually learn from online data, in the offline case, we have a fixed, limited dataset to work from and thus must determine whether we have enough data to confidently cluster users together. To address this challenge, we propose two algorithms: Off-C2LUB, which we show analytically and experimentally outperforms existing methods under limited offline user data, and Off-CLUB, which may incur bias when data is sparse but performs well and nearly matches the lower bound when data is sufficient. We experimentally validate these results on both real and synthetic datasets.
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