Recommender Systems

Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems
Typical recommendation and ranking methods aim to optimize the satisfaction of users, but they are often oblivious to their impact on …
Riku Togashi, Kenshi Abe, Yuta Saito
引用 動画 arXiv Proceedings
Off-Policy Evaluation of Ranking Policies under Diverse User Behavior
Ranking interfaces are everywhere in online platforms. There is thus an ever growing interest in their Off-Policy Evaluation (OPE), …
Haruka Kiyohara, Tatsuya Matsuhiro, Yusuke Narita, Nobuyuki Shimizu, Yasuo Yamamoto, Yuta Saito
引用 コード ポスター スライド arXiv Proceedings
会議報告：The 16th ACM Conference on Recommender Systems（RecSys 2022）
神嶌 敏弘, 齋藤優太
学会誌
Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking
Rankings have become the primary interface of many two-sided markets. Many have noted that the rankings not only affect the …
Yuta Saito, Thorsten Joachims
引用 コード 動画 スライド arXiv 会議録
Off-Policy Evaluation for Large Action Spaces via Embeddings
Off-policy evaluation (OPE) in contextual bandits has seen rapid adoption in real-world systems, since it enables offline evaluation of …
Yuta Saito, Thorsten Joachims
引用 コード 動画 スライド arXiv Proceedings
Towards Resolving Propensity Contradiction in Offline Recommender Learning
We study offline recommender learning from explicit rating feedback in the presence of selection bias. A current promising solution for …
Yuta Saito, Masahiro Nomura
引用 Proceedings
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model
In real-world recommender systems and search engines, optimizing ranking decisions to present a ranked list of relevant items is …
Haruka Kiyohara, Yuta Saito, Tatsuya Matsuhiro, Yusuke Narita, Nobuyuki Shimizu, Yasuo Yamamoto
引用 コード arXiv 会議録
会議報告：The 15th ACM Conference on Recommender Systems（RecSys 2021）
神嶌 敏弘, 齋藤優太
学会誌
Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions
Post-click conversion, a pre-defined action on a web service after a click, is an essential form of feedback, as it directly …
Yuta Saito
引用 コード スライド 会議録
Unbiased Pairwise Learning from Biased Implicit Feedback
Implicit feedback is prevalent in real-world scenarios and is widely used in the construction of recommender systems. However, the …
Yuta Saito
引用 コード 会議録