The paper claims a new family of embedding-based off-policy estimators for ranking policies, but its central unbiasedness theorem is false under the stated assumptions.
We utilized SLOPE to determine the optimal number of dimensions to extract from the first dimension of the embedding
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Off-Policy Evaluation of Ranking Policies via Embedding-Space User Behavior Modeling
The paper claims a new family of embedding-based off-policy estimators for ranking policies, but its central unbiasedness theorem is false under the stated assumptions.