Watching inside the Screen: Digital Activity Monitoring for Task Recognition and Proactive Information Retrieval

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Vuong , T , Jacucci , G & Ruotsalo , T 2017 , ' Watching inside the Screen: Digital Activity Monitoring for Task Recognition and Proactive Information Retrieval ' , Proceedings of ACM on interactive, mobile, wearable and ubiquitous technologies , vol. 1 , no. 3 , 109 . https://doi.org/10.1145/3130974

Title: Watching inside the Screen: Digital Activity Monitoring for Task Recognition and Proactive Information Retrieval
Author: Vuong, Tung; Jacucci, Giulio; Ruotsalo, Tuukka
Other contributor: University of Helsinki, Department of Computer Science
University of Helsinki, Ubiquitous Interaction research group / Giulio Jacucci
University of Helsinki, Department of Computer Science


Date: 2017-09-11
Language: eng
Number of pages: 23
Belongs to series: Proceedings of ACM on interactive, mobile, wearable and ubiquitous technologies
ISSN: 2474-9567
DOI: https://doi.org/10.1145/3130974
URI: http://hdl.handle.net/10138/308882
Abstract: We investigate to what extent it is possible to infer a user’s work tasks by digital activity monitoring and use the task models for proactive information retrieval. Ten participants volunteered for the study, in which their computer screen was monitored and related logs were recorded for 14 days. Corresponding diary entries were collected to provide ground truth to the task detection method. We report two experiments using this data. The unsupervised task detection experiment was conducted to detect tasks using unsupervised topic modeling. The results show an average task detection accuracy of more than 70% by using rich screen monitoring data. The single-trial task detection and retrieval experiment utilized unseen user inputs in order to detect related work tasks and retrieve task-relevant information on-line. We report an average task detection accuracy of 95%, and the corresponding model-based document retrieval with Normalized Discounted Cumulative Gain of 98%. We discuss and provide insights regarding the types of digital tasks occurring in the data, the accuracy of task detection on different task types, and the role of using different data input such as application names, extracted keywords, and bag-of-words representations in the task detection process. We also discuss the implications of our results for ubiquitous user modeling and privacy.
Subject: 113 Computer and information sciences
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