38 citations · 67 across the 5 of their papers we have counts for
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…
A Challenging Multimodal Video Summary: Simultaneously Extracting and Generating Keyframe-Caption Pairs from Video
Keito Kudo, Haruki Nagasawa, Jun Suzuki +1
This paper proposes a practical multimodal video summarization task setting and a dataset to train and evaluate the task. The target task involves summarizing a given video into a…
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…
Deep Learning Based Multi-modal Addressee Recognition in Visual Scenes with Utterances
Thao Minh Le, Nobuyuki Shimizu, Takashi Miyazaki +1
With the widespread use of intelligent systems, such as smart speakers, addressee recognition has become a concern in human-computer interaction, as more and more people expect suc…
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…