Analysis of machine learning techniques for context extraction

Michael Granitzer, Mark Kröll, Christin Seifert, Andreas S. Rath, Nicolas Weber, Olivia Dietzel, Stefanie Lindstaedt

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    14 Citations (Scopus)

    Abstract

    "Context is key" conveys the importance of capturing the digital environment of a knowledge worker. Knowing the user's context offers various possibilities for support, like for example enhancing information delivery or providing work guidance. Hence, user interactions have to be aggregated and mapped to predefined task categories. Without machine learning tools, such an assignment has to be done manually. The identification of suitable machine learning algorithms is necessary in order to ensure accurate and timely classification of the user's context without inducing additional workload. This paper provides a methodology for recording user in-teractions and an analysis of supervised classification models, feature types and feature selection for automatically detecting the current task and context of a user. Our analysis is based on a real world data set and shows the applicability of machine learning techniques. © 2008 IEEE.
    Original languageEnglish
    Title of host publication2008 Third International Conference on Digital Information Management
    PublisherIEEE
    Pages233-240
    Number of pages8
    ISBN (Electronic)978-1-4244-2917-2
    ISBN (Print)978-1-4244-2916-5
    DOIs
    Publication statusPublished - Nov 2008
    Event3rd International Conference on Digital Information Management 2008 - London, United Kingdom
    Duration: 13 Nov 200816 Nov 2008
    Conference number: 3

    Conference

    Conference3rd International Conference on Digital Information Management 2008
    CountryUnited Kingdom
    CityLondon
    Period13/11/0816/11/08

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  • Cite this

    Granitzer, M., Kröll, M., Seifert, C., Rath, A. S., Weber, N., Dietzel, O., & Lindstaedt, S. (2008). Analysis of machine learning techniques for context extraction. In 2008 Third International Conference on Digital Information Management (pp. 233-240). IEEE. https://doi.org/10.1109/ICDIM.2008.4746809