Demo Data from VLDB 2026 / reviewers in the wild / expert
Hyungdong Lee
dblp:57/7185
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1
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 · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › text analysis
keyword extraction |
0.1 | 1 | 2009 | Finding advertising keywords on video scripts · SIGIR 2009 |
Information retrieval
text analysis |
0.1 | 1 | 2009 | Finding advertising keywords on video scripts · SIGIR 2009 |
Information retrieval › multimedia analysis and retrieval
multimedia analysis |
0.0 | 1 | 2009 | Finding advertising keywords on video scripts · SIGIR 2009 |
Information retrieval › multimedia analysis and retrieval
video analysis |
0.0 | 1 | 2009 | Finding advertising keywords on video scripts · SIGIR 2009 |
Methods — techniques the papers use, named apart from their topics
scene-based features · 0.1learning-based feature selection · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | Finding advertising keywords on video scriptsabstractA key to success to contextual in-video advertising is finding advertising keywords on video contents effectively, but there has been little literature in the area so far. This paper presents some preliminary results of our learning-based system that finds relevant advertising keywords on particular scene of video contents using their scripts. The system is trained with not only features proven useful in earlier studies but novel features that reflect the situation of a targeted scene. Experimental results show that the new features are potentially helpful for enhancing the accuracy of keyword extraction for contextual in-video advertising. Jung-Tae Lee, Hyungdong Lee, Hee-Seon Park, Young-In Song, Hae-Chang Rim |
SIGIR | 2 |