research-article

VisGNN: Personalized Visualization Recommendationvia Graph Neural Networks

Authors:

Jane Hoffswell,

Eunyee KohAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 2810 - 2818

https://doi.org/10.1145/3485447.3512001

Published: 25 April 2022 Publication History

Abstract

In this work, we develop a Graph Neural Network (GNN) framework for the problem of personalized visualization recommendation. The GNN-based framework first represents the large corpus of datasets and visualizations from users as a large heterogeneous graph. Then, it decomposes a visualization into its data and visual components, and then jointly models each of them as a large graph to obtain embeddings of the users, attributes (across all datasets in the corpus), and visual-configurations. From these user-specific embeddings of the attributes and visual-configurations, we can predict the probability of any visualization arising from a specific user. Finally, the experiments demonstrated the effectiveness of using graph neural networks for automatic and personalized recommendation of visualizations to specific users based on their data and visual (design choice) preferences. To the best of our knowledge, this is the first such work to develop and leverage GNNs for this problem.

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VisGNN: Personalized Visualization Recommendationvia Graph Neural Networks

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

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Cited By

Coelho DGhai BKrishna AVelez-Rojas MGreenspan SMankovski SMueller K(2024)TaskFinder: A Semantics-Based Methodology for Visualization Task RecommendationAnalytics10.3390/analytics30300153:3(255-275)Online publication date: 4-Jul-2024
https://doi.org/10.3390/analytics3030015
Zhu JWu MZhou YCao NZhu HZhu M(2024)Dowsing: a task-driven approach for multiple-view visualizations dynamic recommendationJournal of Visualization10.1007/s12650-024-00989-927:4(695-712)Online publication date: 17-Apr-2024
https://doi.org/10.1007/s12650-024-00989-9
Lai SLuan WTao J(2023)Explore Your Network in Minutes: A Rapid Prototyping Toolkit for Understanding Neural Networks with Visual AnalyticsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332657530:1(683-693)Online publication date: 3-Nov-2023
https://dl.acm.org/doi/10.1109/TVCG.2023.3326575
Ji DLuo HBao Z(2023)Visualization Recommendation Through Visual Relation Learning and Visual Preference Learning2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00145(1860-1873)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00145
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
Wu XHe HYang HTai YWang ZZhang W(2023)PDA-GNN: propagation-depth-aware graph neural networks for recommendationWorld Wide Web10.1007/s11280-023-01200-z26:5(3585-3606)Online publication date: 8-Aug-2023
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Kuruva HSwarupa Rani KMoiz S(2023)Visualization Recommendation for Incremental Data Based on IntentMulti-disciplinary Trends in Artificial Intelligence10.1007/978-3-031-36402-0_26(285-296)Online publication date: 21-Jul-2023
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Liu YZhong ZChe CZhu Y(2022)Recommendations with residual connections and negative sampling based on knowledge graphsKnowledge-Based Systems10.1016/j.knosys.2022.110049258:COnline publication date: 22-Dec-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.110049

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