Iterative Neural Scoring of Validated Insight Candidates

Allmin Susaiyah; Aki  Harma; Ehud Reiter; Milan  Petkovic

Iterative Neural Scoring of Validated Insight Candidates

Allmin Susaiyah, Aki Harma, Ehud Reiter, Milan Petkovic

Research output: Contribution to conference › Unpublished paper › peer-review

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.

Original language	English
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 Sep 2020 → 7 Sep 2020 https://intellang.github.io/

Conference

Conference	IntelLanG
Country/Territory	Spain
City	Santiago de Compostela
Period	7/09/20 → 7/09/20
Internet address	https://intellang.github.io/

Cite this

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title = "Iterative Neural Scoring of Validated Insight Candidates",

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. Wealso 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.",

author = "Allmin Susaiyah and Aki Harma and Ehud Reiter and Milan Petkovic",

note = "Acknowledgments: This work was supported by the Horizon H2020 Marie Sk{\l}odowska-Curie Actions Initial Training Network European Industrial Doctorates project under grant agreement No. 812882 (PhilHumans).; IntelLanG : Intelligent Information Processing and Natural Language Generation ; Conference date: 07-09-2020 Through 07-09-2020",

year = "2020",

month = aug,

day = "17",

language = "English",

url = "https://intellang.github.io/",

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TY - CONF

T1 - Iterative Neural Scoring of Validated Insight Candidates

AU - Susaiyah, Allmin

AU - Harma, Aki

AU - Reiter, Ehud

AU - Petkovic, Milan

N1 - 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).

PY - 2020/8/17

Y1 - 2020/8/17

N2 - 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. Wealso 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.

AB - 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. Wealso 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.

M3 - Unpublished paper

T2 - IntelLanG

Y2 - 7 September 2020 through 7 September 2020

ER -

Iterative Neural Scoring of Validated Insight Candidates

Abstract

Conference

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