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Steven Garcia

dblp:76/6530 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2018
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-authorDatabases, data management, data science and information retrieval · 1Human-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.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

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

TopicWeightPapersLastEvidence papers
Information retrieval
evaluation
0.112009
A case for improved evaluation of query difficulty prediction · SIGIR 2009
Information retrieval › evaluation
evaluation methodology
0.112009
A case for improved evaluation of query difficulty prediction · SIGIR 2009
Information retrieval › evaluation › query performance prediction
query difficulty estimation
0.112009
A case for improved evaluation of query difficulty prediction · SIGIR 2009

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

correlation coefficient · 0.1TREC runs · 0.1
YearPublicationVenuePosition
2018 Efficient voxelization using projected optimal scanline
Steven Garcia, Weiwei Xu 0003, Tianjia Shao, Yin Yang 0002
Graph. Model.2
2015 Fast image segmentation on mobile phone using multi-level graph cut
Steven Garcia, Patrick Gage Kelley, Yin Yang 0002
Graphics Interface1
2009 A case for improved evaluation of query difficulty prediction
abstract
Query difficulty prediction aims to identify, in advance, how well an information retrieval system will perform when faced with a particular search request. The current standard evaluation methodology involves calculating a correlation coefficient, to indicate how strongly the predicted query difficulty is related with an actual system performance measure, usually Average Precision. We run a series of experiments based on predictors that have been shown to perform well in the literature, comparing these across different TREC runs. Our results demonstrate that the current evaluation methodology is severely limited. Although it can be used to demonstrate the performance of a predictor for a single system, such performance is not consistent over a variety of retrieval systems. We conclude that published results in the query difficulty area are generally not comparable, and recommend that prediction be evaluated against a spectrum of underlying search systems.
Falk Scholer, Steven Garcia
SIGIR2