Demo Data from SIGIR 2026 / reviewers in the wild / expert
Jennifer McGinn
dblp:41/5636
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2008
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 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.
| Human-computer interaction and pervasive computing
1 paper |
Design research and methods · 77% Usability and user experience research · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Design research and methods › user-centered design
persona development |
0.1 | 1 | 2008 | Data-driven persona development · CHI 2008 |
Usability and user experience research
user modeling |
0.0 | 1 | 2008 | Data-driven persona development · CHI 2008 |
Methods — techniques the papers use, named apart from their topics
statistical analysis · 0.1clustering · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Data-driven persona developmentabstractMuch has been written on creating personas --- both what they are good for, and how to create them. A common problem with personas is that they are not based on real customer data, and if they are, the data set is not of a sample size that can be considered statistically significant. In this paper, we describe a new method for creating and validating personas, based on the statistical analysis of data, which is fast and cost effective. Jennifer McGinn, Nalini Kotamraju |
CHI | 1 |