Demo Data from VLDB 2026 / reviewers in the wild / expert
Soichiro Fujita
dblp:245/8770
· DBLP profile ↗
3ranked-venue papers
3as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author
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 · 67% Data mining · 33% | |
| Computer graphics and multimedia
1 paper |
Multimedia analysis and retrieval · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Multimedia analysis and retrieval › video captioning
dense video captioning |
0.4 | 1 | 2020 | SODA: Story Oriented Dense Video Captioning Evaluation Framework · ECCV (6) 2020 |
Multimedia analysis and retrieval
video captioning |
0.4 | 1 | 2020 | SODA: Story Oriented Dense Video Captioning Evaluation Framework · ECCV (6) 2020 |
Information retrieval › ranking › text ranking
comment ranking |
0.4 | 1 | 2019 | Dataset Creation for Ranking Constructive News Comments · ACL (1) 2019 |
Data mining
dataset construction |
0.4 | 1 | 2019 | Dataset Creation for Ranking Constructive News Comments · ACL (1) 2019 |
Information retrieval
ranking |
0.4 | 1 | 2019 | Dataset Creation for Ranking Constructive News Comments · ACL (1) 2019 |
Methods — techniques the papers use, named apart from their topics
pairwise ranking model · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | SODA: Story Oriented Dense Video Captioning Evaluation Framework
Soichiro Fujita, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura, Masaaki Nagata |
ECCV (6) | 1 |
| 2020 | Unsupervised Ensemble of Ranking Models for News Comments Using Pseudo Answers
Soichiro Fujita, Hayato Kobayashi, Manabu Okumura |
ECIR (2) | 1 |
| 2019 | Dataset Creation for Ranking Constructive News CommentsabstractRanking comments on an online news service is a practically important task for the service provider, and thus there have been many studies on this task.However, most of them considered users' positive feedback, such as "Like"-button clicks, as a quality measure.In this paper, we address directly evaluating the quality of comments on the basis of "constructiveness," separately from user feedback.To this end, we create a new dataset including 100K+ Japanese comments with constructiveness scores (C-scores).Our experiments clarify (a) C-scores are not always related to users' positive feedback and (b) the performance of pairwise ranking models tends to be more enhanced by the variation in comments than that in articles. Soichiro Fujita, Hayato Kobayashi, Manabu Okumura |
ACL (1) | 1 |