A Study on Automatic Classification of Users’ Desktop Interactions
Knowledge workers frequently change activities, either by choice or through interruptions. With an increasing number of activities and activity switches, it is becoming more and more difficult for knowledge workers to keep track of their desktop activities. This article presents our
efforts to achieve activity awareness through automatic classification of user's everyday desktop activities. For getting a deeper understanding, we investigate performance of various classifiers with respect to discriminative power of time-, interaction-, and content-based feature sets for
different work scenarios and users. Specifically, by viewing an activity as a sequence of desktop interactions we present (1) a methodology for translating a user's desktop interactions into activities, (2) evaluation of the discriminative power of different activity features and feature types,
and (3) analysis of supervised classification models for classifying desktop activity under two different scenarios, i.e., an activity-centric scenario and a user-centric scenario. The experiments are carried out on a real-world dataset, and the results show satisfactory accuracy using relatively
few and simple types of features.
Keywords: activity awareness; classification; modeling of user behavior; user interaction
Document Type: Research Article
Affiliations: College of Computer Science, Zhejiang University, Hangzhou, P.R. China
Publication date: 04 July 2015
- Information for Authors
- Subscribe to this Title
- Ingenta Connect is not responsible for the content or availability of external websites
- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content