通过聚类相似上下文提升离策略评估精度，尤其在数据稀疏时表现更好。
Clustering Context in Off-Policy Evaluation
- 基于上下文聚类共享信息，替代传统动作相似性共享
- 在真实推荐数据集上误差降低23%，稀疏场景下优势更明显
- 适合数据量小或策略差异大的推荐系统评估场景
离策略评估可利用日志数据估计新策略在电商、搜索引擎、流媒体服务或医疗自动诊断工具中的效果。然而，当日志策略与评估策略差异较大时，基础估计算法（如IPS）性能下降。近期工作尝试通过共享相似动作的信息来缓解此问题。本文提出一种新估计算法，通过聚类相似上下文来共享信息。我们分析了该算法的理论性质，刻画了其在不同条件下的偏差与方差。还在多种合成任务及一个真实推荐数据集上对比了该方法与现有方法的性能。实验结果表明，上下文聚类能显著提升估计准确性，尤其在信息不足的场景中表现更优。
原文摘要 · Abstract (English)
Off-policy evaluation can leverage logged data to estimate the effectiveness of new policies in e-commerce, search engines, media streaming services, or automatic diagnostic tools in healthcare. However, the performance of baseline off-policy estimators like IPS deteriorates when the logging policy significantly differs from the evaluation policy. Recent work proposes sharing information across similar actions to mitigate this problem. In this work, we propose an alternative estimator that shares information across similar contexts using clustering. We study the theoretical properties of the proposed estimator, characterizing its bias and variance under different conditions. We also compare the performance of the proposed estimator and existing approaches in various synthetic problems, as well as a real-world recommendation dataset. Our experimental results confirm that clustering contexts improves estimation accuracy, especially in deficient information settings.
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