Machine Learning based Work Task Classification
Michael Granitzer, Andreas S. Rath, Mark KrÃ¶ll, Christin Seifert, Doris Ipsmiller, Didier Devaurs, Nicolas Weber, Stefanie Lindstaedt
Journal of Digital Information Management, Vol. 7, No. 5, 2009.
DOI: 
Link: http://www.dirf.org/jdim/v3n4a2.asp
Abstract:
Increasing the productivity of a knowledge worker via intelligent applications requires the identification of a userâ€™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.