Granitzer Michael, Kröll Mark, Seifer Christin, Rath Andreas S., Weber Nicolas, Dietzel O., Lindstaedt Stefanie
2008
Analysis of Machine Learning Techniques for Context Extraction
Proceedings of 2008 International Conference on Digital Information Management (ICDIM08) IEEE Computer Society Press
’Context is key’ conveys the importance of capturing thedigital environment of a knowledge worker. Knowing theuser’s context offers various possibilities for support, likefor example enhancing information delivery or providingwork guidance. Hence, user interactions have to be aggregatedand mapped to predefined task categories. Withoutmachine learning tools, such an assignment has to be donemanually. The identification of suitable machine learningalgorithms is necessary in order to ensure accurate andtimely classification of the user’s context without inducingadditional workload.This paper provides a methodology for recording user interactionsand an analysis of supervised classification models,feature types and feature selection for automatically detectingthe current task and context of a user. Our analysisis based on a real world data set and shows the applicabilityof machine learning techniques.