Nazemi, Kawa and Stab, Christian and Fellner, Dieter W. (2010):
Interaction Analysis: An Algorithm for Interaction Prediction and Activity Recognition in Adaptive Systems.
pp. 607-612, IEEE Press, New York, IEEE International Conference on Intelligent Computing and Intelligent Systems. Proceedings, DOI: 10.1109/ICICISYS.2010.5658514,
[Conference or Workshop Item]
Abstract
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*/19-Algorithm improves the Mean Prediction Rank more than 19 compared to other well-established prediction algorithms.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2010 |
| Creators: | Nazemi, Kawa and Stab, Christian and Fellner, Dieter W. |
| Title: | Interaction Analysis: An Algorithm for Interaction Prediction and Activity Recognition in Adaptive Systems |
| Language: | English |
| Abstract: | 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*/19-Algorithm improves the Mean Prediction Rank more than 19 compared to other well-established prediction algorithms. |
| Publisher: | IEEE Press, New York |
| Uncontrolled Keywords: | Forschungsgruppe Semantic Models, Immersive Systems (SMIS), User modeling, Adaptive user interfaces, Interaction analysis, User behavior, Statistics |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Interactive Graphics Systems |
| Event Title: | IEEE International Conference on Intelligent Computing and Intelligent Systems. Proceedings |
| Date Deposited: | 12 Nov 2018 11:16 |
| DOI: | 10.1109/ICICISYS.2010.5658514 |
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