VLDB 2026 Research / reviewers in the wild / expert
Ryusei Ohtani
dblp:402/3480
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 50% Recommender systems · 50% | |
| Artificial intelligence
1 paper |
Trustworthy machine learning · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Recommender systems › explainable recommendation
counterfactual explanation |
0.9 | 1 | 2025 | Counterfactual Explanations of Time Varying Rankings (Student Abstract) · AAAI 2025 |
Information retrieval
ranking |
0.9 | 1 | 2025 | Counterfactual Explanations of Time Varying Rankings (Student Abstract) · AAAI 2025 |
Machine learning › Trustworthy machine learning › interpretability
counterfactual explanation |
0.3 | 1 | 2025 | Counterfactual Explanations of Time Varying Rankings (Student Abstract) · AAAI 2025 |
Machine learning › Trustworthy machine learning
interpretability |
0.3 | 1 | 2025 | Counterfactual Explanations of Time Varying Rankings (Student Abstract) · AAAI 2025 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Counterfactual Explanations of Time Varying Rankings (Student Abstract)abstractCounterfactual explanations in Explainable AI (XAI) identify which features to change to alter an outcome, but existing methods adjust only the features of a single agent. We present a new approach to re-evaluating rankings that is based on predictions of future features of the other agents in a ranking system. It uses an algorithm that provides a more realistic counterfactual explanation of changing the ranking of a particular agent. Computer experiments demonstrated that the proposed algorithm can capture the time variation of the entire ranking system in the inference results. Ryusei Ohtani, Yuko Sakurai, Satoshi Oyama |
AAAI | 1 |
| 2025 | An Efficient Point-of-Interest Placement Method Based on Betweenness Centrality
Ryuta Shiraishi, Ryusei Ohtani, Yuko Sakurai, Satoshi Oyama |
DEXA (2) | 2 |