48 citations · 50 across the 6 of their papers we have counts for
4 papers · 1 filter
Online Evaluation Methods for the Causal Effect of Recommendations
Masahiro Sato
Evaluating the causal effect of recommendations is an important objective because the causal effect on user interactions can directly leads to an increase in sales and user engagem…
Approximation Theory Based Methods for RKHS Bandits
Sho Takemori, Masahiro Sato
The RKHS bandit problem (also called kernelized multi-armed bandit problem) is an online optimization problem of non-linear functions with noisy feedback. Although the problem has…
Unbiased Learning for the Causal Effect of Recommendation
Masahiro Sato, Sho Takemori, Janmajay Singh +1
Increasing users' positive interactions, such as purchases or clicks, is an important objective of recommender systems. Recommenders typically aim to select items that users will i…
Submodular Bandit Problem Under Multiple Constraints
Sho Takemori, Masahiro Sato, Takashi Sonoda +2
The linear submodular bandit problem was proposed to simultaneously address diversified retrieval and online learning in a recommender system. If there is no uncertainty, this prob…