Abstract
Automatic generation of personalised behavioural insight messages is useful in many applications, for example, health selfmanagement services based on a wearable and an app. Insights should be statistically valid, but also interesting and actionable for the user of the service. In this paper, we propose a novel neural network approach for joint modeling of these elements of the relevancy, that is, statistical validity and user preference, using synthetic and real test data sets. We
also demonstrate in an online learning scenario that the system can automatically adapt to the changing preferences of the user while preserving the statistical validity of the mined insights.
also demonstrate in an online learning scenario that the system can automatically adapt to the changing preferences of the user while preserving the statistical validity of the mined insights.
| Original language | English |
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| Number of pages | 10 |
| Publication status | Accepted/In press - 17 Aug 2020 |
| Event | IntelLanG : Intelligent Information Processing and Natural Language Generation - Santiago de Compostela, Spain Duration: 7 Sept 2020 → 7 Sept 2020 https://intellang.github.io/ |
Conference
| Conference | IntelLanG |
|---|---|
| Country/Territory | Spain |
| City | Santiago de Compostela |
| Period | 7/09/20 → 7/09/20 |
| Internet address |
Bibliographical note
Acknowledgments:This work was supported by the Horizon H2020 Marie Skłodowska-Curie Actions Initial Training Network European Industrial Doctorates project under grant agreement No. 812882 (PhilHumans).