Finding "interesting" correlations in multi-faceted personal informatics systems

Abstract

© 2016 Authors. Personal informatics 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 in their data to gain a deeper understanding of the factors that contribute to users' interest. We then use this understanding to identify candidate objective measures for information filtering, which can be applied without input from the user. Finally, we test the predictive power of these m..