| Augmenting knowledge tracing by considering forgetting behavior K Nagatani, Q Zhang, M Sato, YY Chen, F Chen, T Ohkuma The world wide web conference, 3101-3107, 2019 | 242 | 2019 |
| Observation of the Fano-Kondo Antiresonance in a Quantum Wire<? format?> with a Side-Coupled Quantum Dot M Sato, H Aikawa, K Kobayashi, S Katsumoto, Y Iye Physical review letters 95 (6), 066801, 2005 | 182 | 2005 |
| Unbiased learning for the causal effect of recommendation M Sato, S Takemori, J Singh, T Ohkuma Proceedings of the 14th ACM conference on recommender systems, 378-387, 2020 | 74 | 2020 |
| Uplift-based evaluation and optimization of recommenders M Sato, J Singh, S Takemori, T Sonoda, Q Zhang, T Ohkuma Proceedings of the 13th ACM Conference on Recommender Systems, 296-304, 2019 | 39 | 2019 |
| Explaining recommendations using contexts M Sato, B Ahsan, K Nagatani, T Sonoda, Q Zhang, T Ohkuma Proceedings of the 23rd International Conference on Intelligent User …, 2018 | 37 | 2018 |
| Discount sensitive recommender system for retail business M Sato, H Izumo, T Sonoda Proceedings of the 3rd Workshop on Emotions and Personality in Personalized …, 2015 | 21 | 2015 |
| Submodular bandit problem under multiple constraints S Takemori, M Sato, T Sonoda, J Singh, T Ohkuma Conference on Uncertainty in Artificial Intelligence, 191-200, 2020 | 18 | 2020 |
| Model for cooking recipe generation using reinforcement learning J Fujita, M Sato, H Nobuhara 2021 IEEE 37th International Conference on Data Engineering Workshops (ICDEW …, 2021 | 15 | 2021 |
| Modeling Individual Users' Responsiveness to Maximize Recommendation Impact M Sato, H Izumo, T Sonoda Proceedings of the 2016 Conference on User Modeling Adaptation and …, 2016 | 14 | 2016 |
| Exploring an optimal online model for new job recommendation: Solution for recsys challenge 2017 M Sato, K Nagatani, T Tahara Proceedings of the Recommender Systems Challenge 2017, 1-5, 2017 | 13 | 2017 |
| Action-triggering recommenders: Uplift optimization and persuasive explanation M Sato, S Kawai, H Nobuhara 2019 International Conference on Data Mining Workshops (ICDMW), 1060-1069, 2019 | 11 | 2019 |
| Accurate and Diverse Recommendation based on Users' Tendencies toward Temporal Item Popularity. K Nagatani, M Sato RecTemp@ RecSys, 35-39, 2017 | 11 | 2017 |
| Context style explanation for recommender systems M Sato, K Nagatani, T Sonoda, Q Zhang, T Ohkuma Journal of Information Processing 27, 720-729, 2019 | 10 | 2019 |
| Utilizing informative missingness for early prediction of sepsis J Singh, K Oshiro, R Krishnan, M Sato, T Ohkuma, N Kato 2019 Computing in Cardiology (CinC), 1-4, 2019 | 9 | 2019 |
| Online evaluation methods for the causal effect of recommendations M Sato Proceedings of the 15th ACM Conference on Recommender Systems, 96-101, 2021 | 8 | 2021 |
| Causality-aware neighborhood methods for recommender systems M Sato, J Singh, S Takemori, Q Zhang Advances in Information Retrieval: 43rd European Conference on IR Research …, 2021 | 8 | 2021 |
| On missingness features in machine learning models for critical care: observational study J Singh, M Sato, T Ohkuma JMIR Medical Informatics 9 (12), e25022, 2021 | 7 | 2021 |
| Modeling user exposure with recommendation influence M Sato, J Singh, S Takemori, T Sonoda, Q Zhang, T Ohkuma Proceedings of the 35th Annual ACM Symposium on Applied Computing, 1461-1464, 2020 | 7 | 2020 |
| Incorporating Wide Context Information for Deep Knowledge Tracing using Attentional Bi-interaction. R Krishnan, J Singh, M Sato, Q Zhang, T Ohkuma L2D@ WSDM, 1-13, 2021 | 5 | 2021 |
| 60.3: High Resolution Electronic Paper Based on LED Print Head Scanning Exposure M Sato, T Ishii, N Hiji, K Tomoda, S Yamamoto, K Baba SID Symposium Digest of Technical Papers 39 (1), 923-926, 2008 | 5 | 2008 |
