Demo Data from EDBT 2026 / reviewers in the wild / expert
Gijs van Dinther
dblp:23/3017
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2005
—ORCID · none
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 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval
query formulation |
0.1 | 1 | 2005 | Live visual relevance feedback for query formulation · SIGIR 2005 |
Information retrieval
query processing |
0.1 | 1 | 2005 | Live visual relevance feedback for query formulation · SIGIR 2005 |
Information retrieval › interactive information retrieval
user interface design and tools |
0.1 | 1 | 2005 | Live visual relevance feedback for query formulation · SIGIR 2005 |
Information retrieval › search interfaces
visual query interface |
0.1 | 1 | 2005 | Live visual relevance feedback for query formulation · SIGIR 2005 |
Information retrieval
retrieval models |
0.0 | 1 | 2005 | Live visual relevance feedback for query formulation · SIGIR 2005 |
Methods — techniques the papers use, named apart from their topics
visual relevance feedback · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | Live visual relevance feedback for query formulationabstractUsers browsing the Internet seem relatively satisfied with the performance of search engines. An optimistic explanation would be the high quality of search engines. A more pessimistic one would be that people just adapt easily to any new technology. A third explanation is people's ignorance about recall: as they simply don't know what relevant documents are missed, they can hardly be expected to worry about them. And so they easily conceive the result as the best they can get. To allow the user to better assess the quality of the search results, an algorithm was developed that computes a visual representation of the document space in the neighborhood of the user's query.The paper (1) outlines the algorithm, (2) shows how users can explore the neighborhood of a query, and (3) demonstrates how users can guess more judiciously whether they need to further elaborate their query to improve retrieval results. Eduard Hoenkamp, Gijs van Dinther |
SIGIR | 2 |