Off-Policy Evaluation for Ranking Policies under Deterministic Logging Policies
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 method...
| Authors | Koichi Tanaka, Kazuki Kawamura, Takanori Muroi et al. |
|---|---|
| Primary category | cs.LG |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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