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New Adaptive Doubly Robust Method Enhances Off-Policy Evaluation for Ranking Policies

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced Adaptive Doubly Robust (ADR), a novel method for off-policy evaluation (OPE) of ranking policies. ADR aims to reduce the variance and bias inherent in existing OPE techniques like Inverse Propensity Scoring (IPS), Independent IPS (IIPS), and Reward Interaction IPS (RIPS). By adaptively marginalizing importance weights and incorporating reward regression through a control-variate correction, ADR demonstrates improved mean squared error over previous methods in synthetic experiments. AI

IMPACT This research could lead to more accurate evaluation of recommendation and ranking systems, improving their performance and user experience.

RANK_REASON The cluster contains a research paper detailing a new method for off-policy evaluation.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New Adaptive Doubly Robust Method Enhances Off-Policy Evaluation for Ranking Policies

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COVERAGE [2]

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

    Adaptive Doubly Robust Off-Policy Evaluation for Ranking Policies under Diverse User Behavior

    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 ·

    Adaptive Doubly Robust Off-Policy Evaluation for Ranking Policies under Diverse User Behavior

    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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