research-article

Personalized Visualization Recommendation

Authors:

Nesreen K. AhmedAuthors Info & Claims

ACM Transactions on the Web (TWEB), Volume 16, Issue 3

Article No.: 11, Pages 1 - 47

https://doi.org/10.1145/3538703

Published: 19 September 2022 Publication History

Abstract

Visualization recommendation work has focused solely on scoring visualizations based on the underlying dataset, and not the actual user and their past visualization feedback. These systems recommend the same visualizations for every user, despite that the underlying user interests, intent, and visualization preferences are likely to be fundamentally different, yet vitally important. In this work, we formally introduce the problem of personalized visualization recommendation and present a generic learning framework for solving it. In particular, we focus on recommending visualizations personalized for each individual user based on their past visualization interactions (e.g., viewed, clicked, manually created) along with the data from those visualizations. More importantly, the framework can learn from visualizations relevant to other users, even if the visualizations are generated from completely different datasets. Experiments demonstrate the effectiveness of the approach as it leads to higher quality visualization recommendations tailored to the specific user intent and preferences. To support research on this new problem, we release our user-centric visualization corpus consisting of 17.4k users exploring 94k datasets with 2.3 million attributes and 32k user-generated visualizations.

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cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 16, Issue 3

August 2022

155 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/3555790

Editor:
Ryen White
Microsoft Research, USA

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Publication History

Published: 19 September 2022

Online AM: 24 May 2022

Accepted: 22 February 2022

Revised: 09 December 2021

Received: 25 May 2021

Published in TWEB Volume 16, Issue 3

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https://doi.org/10.3390/analytics3030015
Xie YLuo YLi GTang N(2024)HAIChart: Human and AI Paired Visualization SystemProceedings of the VLDB Endowment10.14778/3681954.368199217:11(3178-3191)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3681992
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Liu WZhang ZDing YWang B(2024)Global heterogeneous graph enhanced category-aware attention network for session-based recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122907243:COnline publication date: 25-Jun-2024
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Harris CRossi RMalik SHoffswell JDu FLee TKoh EZhao H(2023)Visual Insight Recommendation: From Ranking Insight Visualizations to Insight Types2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386099(1716-1725)Online publication date: 15-Dec-2023
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