Friedrichs, Björn, Turner, Liam ![]() ![]() ![]() |
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Abstract
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) |
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Date Type: | Published Online |
Status: | Published |
Schools: | 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 |
URI: | https://orca.cardiff.ac.uk/id/eprint/142395 |
Citation Data
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