Insights derived from wearable sensors in smartwatches or sleep trackers can help users in approaching their healthy lifestyle goals. These insights should indicate significant inferences from user behaviour and their generation should adapt automatically to the preferences and goals of the user. In this paper, we propose a neural network model that generates personalised lifestyle insights based on a model of their significance, and feedback from the user. Simulated analysis of our model shows its ability to assign high scores to a) insights with statistically significant behaviour patterns and b) topics related to simple or complex user preferences at any given time. We believe that the proposed neural networks model could be adapted for any application that needs user feedback to score logical inferences from data.
|Number of pages||10|
|Journal||International Journal of Interactive Multimedia and Artificial Intelligence IJIMAI|
|Early online date||15 Feb 2021|
|Publication status||Published - Mar 2021|
This paper is an extension of our previous work presented at the IntelLang workshop at the ECAI 2020 conference . In that, we
considered user preferences that are simple and concerns at-most one type of insights at a time. In this paper, we extend this to two or more
types of similar or different insights being preferred at the same time.
This work was supported by the Horizon H2020 Marie SkłodowskaCurie Actions Initial Training Network European Industrial Doctorates
project under grant agreement No. 812882 (PhilHumans).
- Artificial Intelligence
- Feedback Learning
- Neural Network, Selfsupervised Learning
- Transfer Learning
- Logical Inference, Natural
- Language Generation
- Statistical Learning