Machine Learning based Work Task Classification

Michael Granitzer, Andreas S. Rath, Mark Kröll, Christin Seifert, Doris Ipsmiller, Didier Devaurs, Nicolas Weber, Stefanie Lindstaedt

    Research output: Contribution to journalArticleAcademicpeer-review

    10 Citations (Scopus)
    20 Downloads (Pure)

    Abstract

    Increasing the productivity of a knowledge worker via intelligent applications requires the identification of a user's current work task, i.e. the current work context a user resides in. In this work we present and evaluate machine learning based work task detection methods. By viewing a work task as sequence of digital interaction patterns of mouse clicks and key strokes, we present (i) a methodology for recording those user interactions and (ii) an in-depth analysis of supervised classification models for classifying work tasks in two different scenarios: a task centric scenario and a user centric scenario. We analyze different supervised classification models, feature types and feature selection methods on a laboratory as well as a real world data set. Results show satisfiable accuracy and high user acceptance by using relatively simple types of features.
    Original languageEnglish
    Pages (from-to)306-313
    JournalJournal of digital information management
    Volume7
    Issue number5
    Publication statusPublished - 2009

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