Demo Data from SIGIR 2026 / reviewers in the wild / expert
José Carlos Toucedo
dblp:80/8420
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2011
—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 › document retrieval › domain-specific retrieval
email search |
0.1 | 1 | 2011 | Seeding simulated queries with user-study data forpersonal search evaluation · SIGIR 2011 |
Information retrieval
evaluation |
0.1 | 1 | 2011 | Seeding simulated queries with user-study data forpersonal search evaluation · SIGIR 2011 |
Information retrieval
personalized search |
0.1 | 1 | 2011 | Seeding simulated queries with user-study data forpersonal search evaluation · SIGIR 2011 |
Information retrieval
query formulation |
0.1 | 1 | 2011 | Seeding simulated queries with user-study data forpersonal search evaluation · SIGIR 2011 |
Information retrieval › evaluation › offline evaluation
simulation-based evaluation |
0.1 | 1 | 2011 | Seeding simulated queries with user-study data forpersonal search evaluation · SIGIR 2011 |
Methods — techniques the papers use, named apart from their topics
user study · 0.1logistic regression · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | Seeding simulated queries with user-study data forpersonal search evaluationabstractIn this paper we perform a lab-based user study (n=21) of email re-finding behaviour, examining how the characteristics of submitted queries change in different situations. A number of logistic regression models are developed on the query data to explore the relationship between user- and contextual- variables and query characteristics including length, field submitted to and use of named entities. We reveal several interesting trends and use the findings to seed a simulated evaluation of various retrieval models. Not only is this an enhancement of existing evaluation methods for Personal Search, but the results show that different models are more effective in different situations, which has implications both for the design of email search tools and for the way algorithms for Personal Search are evaluated. David Elsweiler, David E. Losada, José Carlos Toucedo, Ronald T. Fernández |
SIGIR | 3 |