Author

Ren Kishimoto

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#machine learning Preprint Sep 2026

Efficient Offline Learning of Ranking Policies via Top-$k$ Policy Decomposition

Many recommender systems such as for e-commerce and news platforms aim to provide users with rankings they are likely to interact with. Off-Policy Learning (OPL) of ranking policies enables us to learn new ranking policies using only historical logged data. However, ranking settings make OPL remarkably challenging beca...

Ren Kishimoto, Koichi Tanaka, Haruka Kiyohara et al. · 0 citations

#machine learning Preprint Aug 2026

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

Adaptive Doubly Robust (ADR) is proposed, which combines adaptive importance weighting with re- ward regression through a control-variate correction and establishes its unbiasedness when the true user behavior model is observed and characterize a sufficient condition under which it reduces vari- ance relative to AIPS.

Kosuke Iguchi, Ren Kishimoto · 0 citations