Finding “Interesting” Correlations in Multi-Faceted Personal Informatics Systems

Simon Jones, Ryan Kelly

Research output: Contribution to conferencePaper

3 Citations (Scopus)
114 Downloads (Pure)

Abstract

Personal Informatics (PI) systems are capable of uncovering interesting insights about their users by identifying statistical correlations in multi-faceted data. However, they often produce an overwhelming quantity of information. We explore the feasibility of automatically filtering correlational information, based on its interest to users. We analyze users’ subjective ratings of correlations within their data to gain a deeper understanding of the factors that contribute to users’ interest. We then use this understanding to identify candidate measures for information filtering, which can be applied without input from the user. Finally, we test the predictive power of these measures. Our main findings reveal that users in our study valued the Surprisingness and Utility of correlational information above other factors.
Original languageEnglish
Publication statusPublished - 7 May 2016
EventSIGCHI Extended Abstracts on Human Factors in Computing Systems 2016 - California, San Jose, USA United States
Duration: 7 May 2016 → …

Conference

ConferenceSIGCHI Extended Abstracts on Human Factors in Computing Systems 2016
CountryUSA United States
CitySan Jose
Period7/05/16 → …

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