Machine learning based work task classification

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.
Keywords : Task classification
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Article dans une revue
Journal of Digital Information Management, Digital Information Research Foundation, 2009, 7 (5), pp.306-313
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  • HAL Id : hal-00872101, version 1

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Michael Granitzer, Andreas S. Rath, Mark Kröll, Christin Seifert, Doris Ipsmiller, et al.. Machine learning based work task classification. Journal of Digital Information Management, Digital Information Research Foundation, 2009, 7 (5), pp.306-313. 〈hal-00872101〉

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