3 papers
math.OC2026
Distributionally robust optimization for recommendation selection
Tomoya Yanagi, Shunnosuke Ikeda, Ken Kobayashi +1
Recommender systems play an essential role in online services by providing personalized item lists to support users' decision-making processes. While collaborative filtering method…
cs.IR2024
Robust portfolio optimization for recommender systems considering uncertainty of estimated statistics
Tomoya Yanagi, Shunnosuke Ikeda, Yuichi Takano
This paper is concerned with portfolio optimization models for creating high-quality lists of recommended items to balance the accuracy and diversity of recommendations. However, t…
cs.IR2024
Privacy-preserving recommender system using the data collaboration analysis for distributed datasets
Tomoya Yanagi, Shunnosuke Ikeda, Noriyoshi Sukegawa +1
In order to provide high-quality recommendations for users, it is desirable to share and integrate multiple datasets held by different parties. However, when sharing such distribut…