research-article

More bang for your research buck: toward recommender systems for visual analytics

Authors:

Leslie M. Blaha,

Dustin L. Arendt,

Fairul Mohd-ZaidAuthors Info & Claims

BELIV '14: Proceedings of the Fifth Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization

Pages 126 - 133

https://doi.org/10.1145/2669557.2669566

Published: 10 November 2014 Publication History

Abstract

We propose a set of common sense steps required to develop a recommender system for visual analytics. Such a system is an essential way to get additional mileage out of costly user studies, which are typically archived post publication. Crucially, we propose conducting user studies in a manner that allows machine learning techniques to elucidate relationships between experimental data (i.e., user performance) and metrics about the data being visualized and candidate visual representations. We execute a case study within our framework to extract simple rules of thumb that relate different data metrics and visualization characteristics to patterns of user errors on several network analysis tasks. Our case study suggests a research agenda supporting the development of general, robust visualization recommender systems.

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Cited By

Krause JDasgupta AFekete JBertini E(2016)SeekAView: An intelligent dimensionality reduction strategy for navigating high-dimensional data spaces2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)10.1109/LDAV.2016.7874305(11-19)Online publication date: Oct-2016
https://doi.org/10.1109/LDAV.2016.7874305

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More bang for your research buck: toward recommender systems for visual analytics

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cover image ACM Other conferences

BELIV '14: Proceedings of the Fifth Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization

November 2014

184 pages

ISBN:9781450332095

DOI:10.1145/2669557

Editors:
Heidi Lam
Google Inc.
,
Petra Isenberg
INRIA, France
,
Tobias Isenberg
INRIA, France
,
Michael Sedlmair
University of Vienna, Austria

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 10 November 2014

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BELIV '14

BELIV '14: Novel Evaluation Methods For Visualization 2014

November 10, 2014

Paris, France

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BELIV '14 Paper Acceptance Rate 23 of 30 submissions, 77%;

Overall Acceptance Rate 45 of 64 submissions, 70%

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Cited By

Krause JDasgupta AFekete JBertini E(2016)SeekAView: An intelligent dimensionality reduction strategy for navigating high-dimensional data spaces2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)10.1109/LDAV.2016.7874305(11-19)Online publication date: Oct-2016
https://doi.org/10.1109/LDAV.2016.7874305

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