Author

Haruka Kiyohara

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Book Open access Sep 2026

CONSEQUENCES '26 — The 5th Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems

Real-world embodiments of recommender systems are increasingly often posed as decision-making systems, imposing consequences on the world around them. The literature on causal and counterfactual inference allows us to reason about these consequences, and better understand their implications. Whilst this research area h...

Olivier Jeunen, Harrie Oosterhuis, Flavian Vasile et al. · Proceedings of the 20th ACM... · 0 citations

#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