Interaction Analysis: An Algorithm for Interaction Prediction and Activity Recognition in Adaptive Systems
Predictive statistical models are used in the area of adaptive user interfaces to model user behavior and to infer user information from interaction events in an implicit and non-intrusive way. This information constitutes the basis for tailoring the user interface to the needs of the individual user. Consequently, the user analysis process should model the user with information, which can be used in various systems to recognize user activities, intentions and roles to accomplish an adequate adaptation to the given user and his current task. In this paper we present the improved prediction algorithm KO*/19, which is able to recognize, beside interaction predictions, behavioral patterns for recognizing user activities. By means of this extension, the evaluation shows that the KO*/19Algorithm improves the Mean Prediction Rank more than 19％ compared to other well-established prediction algorithms.
Predictive Statistical Model Activity Recognition User Modeling Adaptive User Interfaces
Kawa Nazemi Christian Stab Dieter W.Fellner
3D Knowledge Worlds and Semantics Visualization, Fraunhofer Institute for Computer Graphics Research 3D Knowledge Worlds and Semantics Visualization,Fraunhofer Institute for Computer Graphics Research,
国际会议
厦门
英文
607-612
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）