Demo Data from VLDB 2026 / reviewers in the wild / expert
Matthias S. Reichenbach
dblp:147/9176
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, 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 |
Information retrieval · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
User interface design and tools · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › document retrieval › domain-specific retrieval
mathematical information retrieval |
0.2 | 1 | 2014 | Rendering expressions to improve accuracy of relevance assessment for math search · SIGIR 2014 |
Information retrieval › evaluation
relevance judgment |
0.2 | 1 | 2014 | Rendering expressions to improve accuracy of relevance assessment for math search · SIGIR 2014 |
User interface design and tools › search interface
search result presentation |
0.2 | 1 | 2014 | Rendering expressions to improve accuracy of relevance assessment for math search · SIGIR 2014 |
Methods — techniques the papers use, named apart from their topics
user study · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Rendering expressions to improve accuracy of relevance assessment for math searchabstractFinding ways to help users assess relevance when they search using math expressions is critical for making Mathematical Information Retrieval (MIR) systems easier to use. We designed a study where participants completed search tasks involving mathematical expressions using two different summary styles, and measured response time and relevance assessment accuracy. The control summary style used Google's regular hit formatting where expressions are presented as text (e.g. in LaTeX), while the second summary style renders the math expressions. Participants were undergraduate and graduate students. Participants in the rendered summary style (n=19) had on average a 17.18% higher assessment accuracy than those in the non-rendered summary style (n=19), with no significant difference in response times. Participants in the rendered condition reported having fewer problems reading hits than participants in the control condition. This suggests that users will benefit from search engines that properly render math expressions in their hit summaries. Matthias S. Reichenbach, Anurag Agarwal, Richard Zanibbi |
SIGIR | 1 |