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.
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