research-article

Behavior-driven visualization recommendation

Authors:

Zhen WenAuthors Info & Claims

IUI '09: Proceedings of the 14th international conference on Intelligent user interfaces

Pages 315 - 324

https://doi.org/10.1145/1502650.1502695

Published: 08 February 2009 Publication History

Abstract

We present a novel approach to visualization recommendation that monitors user behavior for implicit signals of user intent to provide more effective recommendation. This is in contrast to previous approaches which are either insensitive to user intent or require explicit, user specified task information. Our approach, called Behavior-Driven Visualization Recommendation (BDVR), consists of two distinct phases: (1) pattern detection, and (2) visualization recommendation. In the first phase, user behavior is analyzed dynamically to find semantically meaningful interaction patterns using a library of pattern definitions developed through observations of real-world visual analytic activity. In the second phase, our BDVR algorithm uses the detected patterns to infer a user's intended visual task. It then automatically suggests alternative visualizations that support the inferred visual task more directly than the user's current visualization. We present the details of BDVR and describe its implementation within our lab's prototype visual analysis system. We also present study results that demonstrate that our approach shortens task completion time and reduces error rates when compared to behavior-agnostic recommendation.

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Cited By

Fu B(2024)Predictive Gaze Analytics: A Comparative Case Study of the Foretelling Signs of User Performance during Interaction with Visualizations of Ontology Class HierarchiesMultimodal Technologies and Interaction10.3390/mti81000908:10(90)Online publication date: 12-Oct-2024
https://doi.org/10.3390/mti8100090
Wang ABorland DGotz D(2024)An empirical study of counterfactual visualization to support visual causal inferenceInformation Visualization10.1177/1473871624122943723:2(197-214)Online publication date: 7-Feb-2024
https://doi.org/10.1177/14738716241229437
Braga DSilva MLima RBarbosa GBarbosa S(2024)VisStoryMaker: supporting non-expert analysts in visually exploring datasets and communicating insights with visual annotations and data storiesProceedings of the XXIII Brazilian Symposium on Human Factors in Computing Systems10.1145/3702038.3702113(1-15)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3702038.3702113
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Index Terms

Behavior-driven visualization recommendation
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Graphical user interfaces

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cover image ACM Conferences

IUI '09: Proceedings of the 14th international conference on Intelligent user interfaces

February 2009

522 pages

ISBN:9781605581682

DOI:10.1145/1502650

General Chairs:
Cristina Conati
University of British Columbia, Canada
,
Mathias Bauer
mineway GmbH, Germany
,
Program Chairs:
Nuria Oliver
Telefonica Research, Spain
,
Dan Weld
University of Washington, USA

Copyright © 2009 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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Published: 08 February 2009

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February 8 - 11, 2009

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Cited By

Fu B(2024)Predictive Gaze Analytics: A Comparative Case Study of the Foretelling Signs of User Performance during Interaction with Visualizations of Ontology Class HierarchiesMultimodal Technologies and Interaction10.3390/mti81000908:10(90)Online publication date: 12-Oct-2024
https://doi.org/10.3390/mti8100090
Wang ABorland DGotz D(2024)An empirical study of counterfactual visualization to support visual causal inferenceInformation Visualization10.1177/1473871624122943723:2(197-214)Online publication date: 7-Feb-2024
https://doi.org/10.1177/14738716241229437
Braga DSilva MLima RBarbosa GBarbosa S(2024)VisStoryMaker: supporting non-expert analysts in visually exploring datasets and communicating insights with visual annotations and data storiesProceedings of the XXIII Brazilian Symposium on Human Factors in Computing Systems10.1145/3702038.3702113(1-15)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3702038.3702113
Yanez FConati COttley ANobre C(2024)The State of the Art in User‐Adaptive VisualizationsComputer Graphics Forum10.1111/cgf.15271Online publication date: 4-Dec-2024
https://doi.org/10.1111/cgf.15271
Koenen JPetersen MGarth CGerrits T(2024)DaVE - A Curated Database of Visualization Examples2024 IEEE Visualization and Visual Analytics (VIS)10.1109/VIS55277.2024.00010(11-15)Online publication date: 13-Oct-2024
https://doi.org/10.1109/VIS55277.2024.00010
Wang ABorland DPeck TWang WGotz D(2024)Causal Priors and Their Influence on Judgements of Causality in Visualized DataIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345638131:1(765-775)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456381
Wang ABorland DGotz D(2024)Beyond Correlation: Incorporating Counterfactual Guidance to Better Support Exploratory Visual AnalysisIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345636931:1(776-786)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456369
Wu GGuo SHoffswell JChan GRossi RKoh E(2024)Socrates: Data Story Generation via Adaptive Machine-Guided Elicitation of User FeedbackIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332736330:1(131-141)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327363
Shi YChen BChen YJin ZXu KJiao XGao TCao N(2024)Supporting Guided Exploratory Visual Analysis on Time Series Data with Reinforcement LearningIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332720030:1(1172-1182)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327200
Ceneda DCollins CEl-Assady MMiksch STominski CArleo A(2024)A Heuristic Approach for Dual Expert/End-User Evaluation of Guidance in Visual AnalyticsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332715230:1(997-1007)Online publication date: 1-Jan-2024
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