38 citations · 67 across the 5 of their papers we have counts for
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cs.LG2026
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…
cs.LG2018★ 11 cited
Cross-domain Recommendation via Deep Domain Adaptation
Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu +2
The behavior of users in certain services could be a clue that can be used to infer their preferences and may be used to make recommendations for other services they have never use…