This case study evaluates to what extent personal preferences can be automatically derived from user event data in an mHealth setting. Based on a theoretical framework, user preferences are described using six classes. Based on this framework, a structure of six Takagi-Sugeno fuzzy inference systems was constructed and evaluated against baseline data from an official survey for measuring the framework's constructs. From this analysis, it was found that user preferences may be derived from user event data using fuzzy modeling with accuracy scores that are higher than a random predictor would typically achieve.
|Titel||2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)|
|ISBN van elektronische versie||978-94-6252-770-6|
|Status||Gepubliceerd - sep 2019|
|Evenement||11th Conference of the European Society for Fuzzy Logic and Technology - Prague, Tsjechië|
Duur: 9 sep 2019 → 13 sep 2019
|Congres||11th Conference of the European Society for Fuzzy Logic and Technology|
|Periode||9/09/19 → 13/09/19|