38 citations · 46 across the 3 of their papers we have counts for
4 papers
Efficient Offline Learning of Ranking Policies via Top- Policy Decomposition
Ren Kishimoto, Koichi Tanaka, Haruka Kiyohara +4
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 policie…
Off-Policy Learning with Limited Supply
Koichi Tanaka, Ren Kishimoto, Bushun Kawagishi +4
We study off-policy learning (OPL) in contextual bandits, which plays a key role in a wide range of real-world applications such as recommendation systems and online advertising. T…
Off-Policy Evaluation of Ranking Policies under Diverse User Behavior
Haruka Kiyohara, Masatoshi Uehara, Yusuke Narita +3
Ranking interfaces are everywhere in online platforms. There is thus an ever growing interest in their Off-Policy Evaluation (OPE), aiming towards an accurate performance evaluatio…
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model
Haruka Kiyohara, Yuta Saito, Tatsuya Matsuhiro +3
In real-world recommender systems and search engines, optimizing ranking decisions to present a ranked list of relevant items is critical. Off-policy evaluation (OPE) for ranking p…