Finding "Interesting" Correlations in Multi-Faceted Personal Informatics Systems

Simon L. Jones, Ryan Kelly
2016 Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA '16  
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
more » ... erest. 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 measures. Our main findings reveal that users in our study valued the Surprisingness, Utility and Positive Valence of correlational information above other factors.
doi:10.1145/2851581.2892401 dblp:conf/chi/JonesK16 fatcat:drqqfsuf7zg37lnskewgwuowxy