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ARXIV:2603.21485 · ALGORITHMIC RANKING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.21485ALGORITHMIC RANKINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEKoichi Tanaka · Kazuki Kawamura · Takanori Muroi · Yusuke Narita · Yuki Sasamoto · Kei Tateno · +3 at arXiv
Develops novel off-policy evaluation estimators for ranking systems that overcome limitations of deterministic logging policies by leveraging user click behavior.
Opportunity summary
Pain Develops novel off-policy evaluation estimators for ranking systems that overcome limitations of deterministic logging policies by leveraging user click behavior.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Develops novel off-policy evaluation estimators for ranking systems that overcome limitations of deterministic logging policies by leveraging user click behavior. Existing estimators, such as the ranking-wise and position-wise inverse propensity score (IPS) estimators, require…
Off-Policy Evaluation (OPE) is an important practical problem in algorithmic ranking systems, where the goal is to estimate the expected performance of a new ranking policy using only offline logged data collected under a…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We provide theoretical analyses of the bias and variance properties of the proposed estimators and show, through synthetic and real-world experiments, that our estimators…
Algorithmic Ranking moved forward this cycle; last verified April 2026. Public score 4.0/10.
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Develops novel off-policy evaluation estimators for ranking systems that overcome limitations of deterministic logging policies by leveraging user click behavior.
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Paper Pack
10.48550/arXiv.2603.21485Develops novel off-policy evaluation estimators for ranking systems that overcome limitations of deterministic logging policies by leveraging user click behavior.
Abstract
Off-Policy Evaluation (OPE) is an important practical problem in algorithmic ranking systems, where the goal is to estimate the expected performance of a new ranking policy using only offline logged data collected under a different, logging policy. Existing estimators, such as the ranking-wise and position-wise inverse propensity score (IPS) estimators, require the data collection policy to be sufficiently stochastic and suffer from severe bias when the logging policy is fully deterministic. In this paper, we propose novel estimators, Click-based Inverse Propensity Score (CIPS), exploiting the intrinsic stochasticity of user click behavior to address this challenge. Unlike existing methods that rely on the stochasticity of the logging policy, our approach uses click probability as a new form of importance weighting, enabling low-bias OPE even under deterministic logging policies where existing methods incur substantial bias. We provide theoretical analyses of the bias and variance properties of the proposed estimators and show, through synthetic and real-world experiments, that our estimators achieve significantly lower bias compared to strong baselines, for a range of experimental settings with completely deterministic logging policies.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
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Viability
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Dimensions overall score 4.0
PROBLEM
Develops novel off-policy evaluation estimators for ranking systems that overcome limitations of deterministic logging policies by leveraging user click behavior. Existing estimators, such as the ranking-wise and position-wise inverse propensity score (IPS) estimators, require t...
METHOD
Off-Policy Evaluation (OPE) is an important practical problem in algorithmic ranking systems, where the goal is to estimate the expected performance of a new ranking policy using only offline logged data collected under a different, logging policy. Existing estimators, such as t...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We provide theoretical analyses of the bias and variance properties of the proposed estimators and show, through synthetic and real-world experiments, that our estimators achieve significantly lower bias...
WHY NOW
Algorithmic Ranking moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Develops novel off-policy evaluation estimators for ranking systems that overcome limitations of deterministic logging policies by leveraging user click behavior. Existing estimators, such as the ranking-wise and position-wise inverse propensity score (IPS) estimators, require the data collection policy to be sufficiently stochastic and suffer from severe bias when the logging policy is fully deterministic.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Off-Policy Evaluation (OPE) is an important practical problem in algorithmic ranking systems, where the goal is to estimate the expected performance of a new ranking policy using only offline logged data collected under a different, logging policy. Existing estimators, such as the ranking-wise and position-wise inverse propensity score (IPS) estimators, require the data collection policy to be sufficiently stochastic and suffer from severe bias when the logging policy is fully deterministic.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We provide theoretical analyses of the bias and variance properties of the proposed estimators and show, through synthetic and real-world experiments, that our estimators achieve significantly lower bias compared to strong baselines, for a range of experimental settings with completely deterministic logging policies.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Algorithmic Ranking moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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Develops novel off-policy evaluation estimators for ranking systems that overcome limitations of deterministic logging policies by leveraging user click behavior.
Segment
Algorithmic Ranking
Adoption evidence
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Commercial read
4.0/10 public viability
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reason
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proof status
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Artifact maturity
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Technical feasibility
partial
Current read
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Evidence
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Classify regulatory flags before commercialization planning.
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ARTIFACTS
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DEFENSIBILITY
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