6 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 Evaluation for Ranking Policies under Deterministic Logging Policies
Koichi Tanaka, Kazuki Kawamura, Takanori Muroi +6
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 o…
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…
Combinatorial Allocation Bandits with Nonlinear Arm Utility
Yuki Shibukawa, Koichi Tanaka, Yuta Saito +1
A matching platform is a system that matches participants of different types, such as companies and job-seekers. In such a platform, maximizing matches may concentrate assignments…
Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching
Ren Kishimoto, Rikiya Takehi, Koichi Tanaka +4
On two-sided matching platforms such as online dating and recruiting, recommendation algorithms often aim to maximize the total number of matches. However, this objective creates a…
Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits
Tatsuhiro Shimizu, Koichi Tanaka, Ren Kishimoto +3
We explore off-policy evaluation and learning (OPE/L) in contextual combinatorial bandits (CCB), where a policy selects a subset in the action space. For example, it might choose a…