VisGuide: User-Oriented Recommendations for Data Event Extraction

Yu Rong Cao, Xiao Han Li, Jia Yu Pan, Wen Chieh Lin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Data exploration systems have become popular tools with which data analysts and others can explore raw data and organize their observations. However, users of such systems who are unfamiliar with their datasets face several challenges when trying to extract data events of interest to them. Those challenges include progressively discovering informative charts, organizing them into a logical order to depict a meaningful fact, and arranging one or more facts to illustrate a data event. To alleviate them, we propose VisGuide - a data exploration system that generates personalized recommendations to aid users' discovery of data events in breadth and depth by incrementally learning their data exploration preferences and recommending meaningful charts tailored to them. As well as user preferences, VisGuide's recommendations simultaneously consider sequence organization and chart presentation. We conducted two user studies to evaluate 1) the usability of VisGuide and 2) user satisfaction with its recommendation system. The results of those studies indicate that VisGuide can effectively help users create coherent and user-oriented visualization trees that represent meaningful data events.

Original languageEnglish
Title of host publicationCHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450391573
DOIs
StatePublished - 29 Apr 2022
Event2022 CHI Conference on Human Factors in Computing Systems, CHI 2022 - Virtual, Online, United States
Duration: 30 Apr 20225 May 2022

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2022 CHI Conference on Human Factors in Computing Systems, CHI 2022
Country/TerritoryUnited States
CityVirtual, Online
Period30/04/225/05/22

Keywords

  • Visualization Recommendation
  • Visualization Sequencing
  • Visualization Trees

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