New framework optimizes logging policies for off-policy evaluation accuracy

Researchers have developed a new framework for designing logging policies to improve the accuracy of off-policy evaluation (OPE). OPE is crucial for estimating the performance of new policies, like recommender systems, using data collected by existing ones. The study identifies a key tradeoff between reward coverage and variance, proposing optimal logging policies for various scenarios where target policies and reward distributions are known, unknown, or partially known. The findings offer practical guidance for firms selecting recommendation systems and emphasize the importance of treatment selection in data gathering for OPE. AI

影响 Provides theoretical underpinnings for improving the evaluation of AI systems, particularly in recommendation and experimentation.

排序理由 Academic paper detailing a new framework and theoretical results. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

• Logging Policy
• Recommender System
• Off-Policy Evaluation

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