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Demo Data from VLDB 2026 / reviewers in the wild / expert

Seth Hettich

dblp:16/1386 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2006
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 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
Data mining · 44% Information retrieval · 44% Recommender systems · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval › search engines › expert finding
expertise modeling		 0.112006
Mining for proposal reviewers: lessons learned at the national science foundation · KDD 2006
Data mining › text mining
information extraction		 0.112006
Mining for proposal reviewers: lessons learned at the national science foundation · KDD 2006
Recommender systems › user recommendation
reviewer assignment		 0.012006
Mining for proposal reviewers: lessons learned at the national science foundation · KDD 2006

Methods — techniques the papers use, named apart from their topics

text mining · 0.1
YearPublicationVenuePosition
2006 Mining for proposal reviewers: lessons learned at the national science foundation
abstract
In this paper, we discuss a prototype application deployed at the U.S. National Science Foundation for assisting program directors in identifying reviewers for proposals. The application helps program directors sort proposals into panels and find reviewers for proposals. To accomplish these tasks, it extracts information from the full text of proposals both to learn about the topics of proposals and the expertise of reviewers. We discuss a variety of alternatives that were explored, the solution that was implemented, and the experience in using the solution within the workflow of NSF.
Seth Hettich, Michael J. Pazzani
 KDD1