Proposing design recommendations for an intelligent recommender system logging stress

Visuri, Aku; Poguntke, Romina; Kuosmanen, Elina (2018-11-25)
 
Avaa tiedosto
Lataukset: 

URL:
https://doi.org/10.1145/3282894.3289733

Visuri, Aku
Poguntke, Romina
Kuosmanen, Elina
Association for Computing Machinery
25.11.2018

Aku Visuri, Romina Poguntke, and Elina Kuosmanen. 2018. Proposing Design Recommendations for an Intelligent Recommender System Logging Stress. In Proceedings of the 17th International Conference on Mobile and Ubiquitous Multimedia (MUM 2018), Slim Abdennadher and Florian Alt (Eds.). ACM, New York, NY, USA, 411-417. DOI: https://doi.org/10.1145/3282894.3289733

https://rightsstatements.org/vocab/InC/1.0/
© 2018 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceeding MUM 2018 Proceedings of the 17th International Conference on Mobile and Ubiquitous Multimedia, http://dx.doi.org/10.1145/3282894.3289733.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1145/3282894.3289733
Näytä kaikki kuvailutiedot
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2019041111998
Tiivistelmä

Abstract

The connection between stress and smartphone usage behavior has been investigated extensively. While the prediction results using machine learning are encouraging, the challenge of how to cope with data loss remains. Addressing this problem, we propose an Intelligent Recommender System for logging stress based on adding a subjective user data-based validation to predictions made by intelligent algorithms. In a user study involving 731 daily stress self-reports from 30 participants we found discrepancies between subjective and smartphone usage data, i.e. battery, call information, or network usage. Despite the good prediction accuracy of 65% using a Random Forest classifier, combining both information would be beneficial for avoiding data and improving prediction accuracy. For realizing such a system (i.e., a mobile application), we propose three design recommendations, based on the capabilities of frequently used machine learning classifiers, enabling users to annotate their daily stress levels with a predict-and-validate methodology.

Kokoelmat