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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…