research-article

VizRec: Recommending Personalized Visualizations

Authors:

Christoph TrattnerAuthors Info & Claims

ACM Transactions on Interactive Intelligent Systems (TiiS), Volume 6, Issue 4

Article No.: 31, Pages 1 - 39

https://doi.org/10.1145/2983923

Published: 07 November 2016 Publication History

Abstract

Visualizations have a distinctive advantage when dealing with the information overload problem: Because they are grounded in basic visual cognition, many people understand them. However, creating proper visualizations requires specific expertise of the domain and underlying data. Our quest in this article is to study methods to suggest appropriate visualizations autonomously. To be appropriate, a visualization has to follow known guidelines to find and distinguish patterns visually and encode data therein. A visualization tells a story of the underlying data; yet, to be appropriate, it has to clearly represent those aspects of the data the viewer is interested in. Which aspects of a visualization are important to the viewer? Can we capture and use those aspects to recommend visualizations? This article investigates strategies to recommend visualizations considering different aspects of user preferences. A multi-dimensional scale is used to estimate aspects of quality for visualizations for collaborative filtering. Alternatively, tag vectors describing visualizations are used to recommend potentially interesting visualizations based on content. Finally, a hybrid approach combines information on what a visualization is about (tags) and how good it is (ratings). We present the design principles behind VizRec, our visual recommender. We describe its architecture, the data acquisition approach with a crowd sourced study, and the analysis of strategies for visualization recommendation.

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Cited By

Wambecke JLogre IMurgue A(2023)Catalogue Visu: a Tool for Fast Visualization PrototypingProceedings of the 34th Conference on l'Interaction Humain-Machine10.1145/3583961.3583969(1-10)Online publication date: 3-Apr-2023
https://dl.acm.org/doi/10.1145/3583961.3583969
Zhou ZWang WGuo MWang YGotz D(2023)A Design Space for Surfacing Content Recommendations in Visual Analytic PlatformsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.320944529:1(84-94)Online publication date: Jan-2023
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Shen LShen ELuo YYang XHu XZhang XTai ZWang J(2023)Towards Natural Language Interfaces for Data Visualization: A SurveyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.314800729:6(3121-3144)Online publication date: 1-Jun-2023
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Index Terms

VizRec: Recommending Personalized Visualizations
1. Human-centered computing
2. Information systems
  1. World Wide Web
    1. Web searching and information discovery
      1. Content ranking
      2. Personalization

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Published In

cover image ACM Transactions on Interactive Intelligent Systems

ACM Transactions on Interactive Intelligent Systems Volume 6, Issue 4

Special Issue on Human Interaction with Artificial Advice Givers

December 2016

176 pages

ISSN:2160-6455

EISSN:2160-6463

DOI:10.1145/3015563

Editor:
Michelle Zhou
Juji, Inc., USA

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Copyright © 2016 ACM.

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Association for Computing Machinery

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Publication History

Published: 07 November 2016

Accepted: 01 March 2016

Revised: 01 January 2016

Received: 01 July 2015

Published in TIIS Volume 6, Issue 4

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Cited By

Wambecke JLogre IMurgue A(2023)Catalogue Visu: a Tool for Fast Visualization PrototypingProceedings of the 34th Conference on l'Interaction Humain-Machine10.1145/3583961.3583969(1-10)Online publication date: 3-Apr-2023
https://dl.acm.org/doi/10.1145/3583961.3583969
Zhou ZWang WGuo MWang YGotz D(2023)A Design Space for Surfacing Content Recommendations in Visual Analytic PlatformsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.320944529:1(84-94)Online publication date: Jan-2023
https://doi.org/10.1109/TVCG.2022.3209445
Shen LShen ELuo YYang XHu XZhang XTai ZWang J(2023)Towards Natural Language Interfaces for Data Visualization: A SurveyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.314800729:6(3121-3144)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TVCG.2022.3148007
Sharaf MMafrur RZuccon G(2023)Efficient Diversification for Recommending Aggregate Data VisualizationsIEEE Access10.1109/ACCESS.2023.328345711(62261-62280)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3283457
Schreck TMutlu BStreit M(2023)Visual Data Science for Industrial ApplicationsDigital Transformation10.1007/978-3-662-65004-2_18(447-471)Online publication date: 3-Feb-2023
https://doi.org/10.1007/978-3-662-65004-2_18
Qian XRossi RDu FKim SKoh EMalik SLee TAhmed N(2022)Personalized Visualization RecommendationACM Transactions on the Web10.1145/353870316:3(1-47)Online publication date: 19-Sep-2022
https://dl.acm.org/doi/10.1145/3538703
Ojo FRossi RHoffswell JGuo SDu FKim SXiao CKoh E(2022)VisGNN: Personalized Visualization Recommendationvia Graph Neural NetworksProceedings of the ACM Web Conference 202210.1145/3485447.3512001(2810-2818)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3485447.3512001
Crisan AFisher SGardy JMunzner T(2022)GEViTRec: Data Reconnaissance Through Recommendation Using a Domain-Specific Visualization Prevalence Design SpaceIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.310774928:12(4855-4872)Online publication date: 1-Dec-2022
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Wang QChen ZWang YQu H(2022)A Survey on ML4VIS: Applying Machine Learning Advances to Data VisualizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.310614228:12(5134-5153)Online publication date: 1-Dec-2022
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Sevilla JCasanova-Salas PCasas-Yrurzum SPortalés C(2021)Multi-Purpose Ontology-Based Visualization of Spatio-Temporal Data: A Case Study on Silk HeritageApplied Sciences10.3390/app1104163611:4(1636)Online publication date: 11-Feb-2021
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