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Discovering types of smartphone usage sessions from user-app interactions

Friedrichs, Björn, Turner, Liam ORCID: and Allen, Stuart ORCID: 2021. Discovering types of smartphone usage sessions from user-app interactions. Presented at: 5th International Workshop on Annotation of useR Data for UbiquitOUs Systems (ARDUOUS 2021), Kassel, Germany, 22-26 March 2021. 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, pp. 459-464. 10.1109/PERCOMWORKSHOPS51409.2021.9431034

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Understanding how and why people use their smartphones has enabled use cases ranging from correlating behaviour with psychological states through to on-device tasks such as app recommendations. However, being able to effectively and pervasively capture usage behaviour is challenging due to the wide range of functions, apps and interactions that are possible. In this paper, we examine how embedding physical user-app activity (e.g., taps and scrolls) can provide a rich basis for summarising device usage. Using a large dataset of 82,758,449 interaction events from 86 users over an 8-week period we combine feature embedding and unsupervised learning to extract prominent interactions within clusters of smartphone usage sessions. We find that high-level features such as session length, unlock state, and app switches are not representative of these clusters and can give a false sense of similarity or dissimilarity between sessions. The results motivate further exploration of the utility of using user-app interaction behaviour as the basis for the aforementioned use cases.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Publisher: IEEE
ISBN: 9781665404242
Date of First Compliant Deposit: 7 July 2021
Date of Acceptance: 15 January 2021
Last Modified: 28 Apr 2023 06:27

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