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English(EN) Adaptive Doubly Robust Off-Policy Evaluation for Ranking Policies under Diverse User Behavior

新的自适应双重稳健方法增强了排序策略的离线策略评估

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种用于排序策略离线策略评估（OPE）的新方法——自适应双重稳健（ADR）。ADR旨在减少现有OPE技术（如逆倾向评分（IPS）、独立IPS（IIPS）和奖励交互IPS（RIPS））固有的方差和偏差。通过自适应地边际化重要性权重并结合奖励回归，通过控制变量校正，ADR在合成实验中证明了比以前的方法有更低的均方误差。 AI

影响 这项研究可能有助于更准确地评估推荐和排序系统，从而提高它们的性能和用户体验。

排序理由 该集群包含一篇详细介绍新的离线策略评估方法的论文。

在 arXiv cs.LG 阅读 →

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新的自适应双重稳健方法增强了排序策略的离线策略评估

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该集群包含一篇详细介绍新的离线策略评估方法的论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Kosuke Iguchi, Ren Kishimoto ·

    面向多样化用户行为的排序策略的自适应双重稳健离轨策略评估

    arXiv:2608.29600v1 Announce Type: new Abstract: Off-policy evaluation (OPE) of ranking policies is challenging be- cause selecting and ordering multiple items from a candidate set makes the number of possible rankings grow combinatorially with the number of candidates and the ran…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Ren Kishimoto ·

    面向多样化用户行为的排序策略的自适应双重稳健离轨策略评估

    Off-policy evaluation (OPE) of ranking policies is challenging be- cause selecting and ordering multiple items from a candidate set makes the number of possible rankings grow combinatorially with the number of candidates and the ranking length. Consequently, Inverse Propensity Sc…

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