Analysis of machine learning techniques for context extraction

"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.